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Future wireless networks require channel coding schemes that can provide high reliability, low latency, and strong adaptability under finite-blocklength and structurally heterogeneous transmission scenarios. Globally coupled low-density parity-check (GC-LDPC) codes are promising for such systems because their coupled structure can enhance error-correction capability, but the additional global constraints also increase decoding complexity and make conventional fixed-parameter decoders less effective. This paper proposes a prior-assisted hierarchical alternating direction method of multipliers (ADMMs) decoding framework for GC-LDPC codes. The proposed decoder first partitions the GC-LDPC parity-check structure into two local subgraphs and performs tuned ADMM decoding on the local blocks in parallel. The local decoding outputs are then merged and verified by the full GC-LDPC parity-check matrix. If the merged local decision satisfies all global constraints, it is directly accepted, thereby avoiding unnecessary full-graph decoding. Otherwise, a global fallback ADMM decoder is activated. In this stage, the channel log-likelihood ratios are fused with soft priors extracted from the local ADMM outputs, where prior clipping and conflict scaling are introduced to control unreliable or contradictory local information. The resulting fused reliability information is used to guide full-matrix ADMM decoding. This local-to-global strategy reduces unnecessary global iterations while preserving the ability to enforce global consistency when local decoding is insufficient. Simulation-oriented metrics, including bit error rate, frame error rate, local pass rate, global fallback rate, global fallback success rate, and average iteration count, are used to evaluate reliability and decoding efficiency. The proposed framework provides an average-complexity-aware and reliability-aware decoding approach for advanced channel coding in future wireless networks.

Integrating patient-reported outcomes and clinical data with external sources such as Prescription Drug Monitoring Programs (PDMPs) enables comprehensive evaluation of opioid prescribing and outcomes. The authors describe the implementation of a secure, honest broker system by the Overdose Prevention Engagement Network to link state-wide registry data with Michigan's PDMP. A trusted intermediary facilitated linkage via a process approved by the institutional review board. Merged data were encrypted and coded without personal identifiers before being released to investigators, ensuring patient privacy and compliance with the Health Insurance Portability and Accountability Act. The linkage process excluded only 0.7 to 2.0% of patient records, demonstrating high performance. Since implementation, 11 peer-reviewed publications have used the linked datasets to advance opioid stewardship research. This novel, privacy-preserving framework provides a scalable and replicable model for diverse cross-system data integration while maintaining patient confidentiality.

Following the introduction of direct-acting antivirals (DAAs), elimination of hepatitis C virus (HCV) became a target of the World Health Organisation (WHO) in 2016. Denmark had a HCV prevalence of 0.21% in 2016, and in 2018, unrestricted access to DAAs was introduced together with intensified testing strategies. To estimate adult HCV diagnostic and treatment coverage in Denmark by the end of 2022. Four national HCV source registers (laboratory reports, surveillance notifications, treatment database and hospital diagnoses) were merged. Capture-recapture analysis was used to estimate diagnosed individuals not identified in the registers, and estimates were adjusted for treated patients. The undiagnosed population was estimated using HCV testing data among high-risk individuals recorded in the Register for Treatment of Drug Use and compared with Danish studies on the diagnosed fraction of newly detected HCV cases. Across the four registers, 1,649 valid records of HCV-infected adults alive and resident in Denmark were identified. Capture-recapture analysis estimated a total of 1,700 (95% CI: 1,677-1,739) diagnosed individuals. With an undiagnosed fraction of 22-32%, the total HCV-infected population was estimated at 2,471-2,582 individuals, corresponding to a prevalence of 0.05-0.06% by the end of 2022. Compared to the 2016 baseline 82% had been diagnosed and 77% of diagnosed patients had been treated for HCV. HCV prevalence in Denmark has declined substantially, and with ongoing national initiatives to test and treat the remaining HCV patients we are likely to achieve WHO targets of diagnostic coverage and treatment before 2030.

We suggest that accelerated reactions in confined volumes, specifically in microdroplets that contain water in contact with air, represent a remarkable, indeed a revolutionary chemical phenomenon. This strong claim is justified by the magnitude of the acceleration effect, by evidence for impact in key areas like reaction screening for drug discovery, by its alignment with sustainable chemistry and by the range of natural phenomena, including geological as well as atmospheric processes, where the effect operates. Accelerated processes in microdroplets enable the rapid conversion of quartz microparticles into hydrophilic silica nanoparticles, the late-stage functionalization of complex biomolecules, and the synthesis of heterocyclic compounds through rapid, environmentally benign 'green' processes that contrast strongly with conventional metal-catalyzed routes. The phenomenon is so unexpected, so unusual, that it has raised skepticism ─ as indeed it should. Compelling evidence for its operation is found in automated high-throughput (HT) experiments in which thousands of reactions are carried out at rates of 1 reaction/second and where accelerated processes occur in microdroplets generated from reaction mixtures during their millisecond flight times to give products that are identified by online mass spectrometry (MS) or deposited on surfaces and then bioassayed. Additional evidence comes from early studies of organic 'name' reactions while parallel evidence for just how remarkable this 'water and air' chemistry is comes from the transformations of N2. This classically unreactive, diatomic molecule can be oxidized to NO2 or reduced to NH3 using nothing but the special interfacial properties of wet microdroplets. The oxidation state of nitrogen ranges from +4 to -3 in these products, and intermediates with oxidation numbers lying within this range are also observed. Just as remarkably, microparticles of minerals, suspended in microdroplets of water, are broken down to generate nanoparticles simply by spraying the mixture and collecting the spray. Evidence that Si-O bonds are cleaved by the superacidic character of the microdroplets stands in contrast with the fact that nanomaterials can be built up by deposition of solvated ions ─ both processes occurring in sprayed microdroplets. Further evidence for the remarkable range of chemistry that occurs under apparently mild conditions lies in the fact that amino acids can be condensed to create peptides in sprayed droplets, while other esters and PFAS 'forever' chemicals can be rapidly hydrolyzed in the same medium. Microdroplet chemistry lies at the convergence of three chronologically distinct but methodologically intersecting domains: accelerated molecular reactions, interfacial inorganic chemistry, and materials evolution under nonequilibrium conditions. Each strand emerged independently, yet microdroplets provide a common physical platform in which they merge through shared interfacial and dynamical principles. From these foundations, the field has naturally expanded in diverse chemical and materials directions. One strand emphasizes accelerated chemical reactivity, where bond-forming reactions proceed on millisecond time scales with selectivity and efficiency, enabling rapid synthetic transformations relevant to pharmaceuticals and fine chemicals. A second strand focuses on inorganic and gas-liquid interfacial chemistry, including the formation of small molecules and ions such as NH3, NO2, sulfates, and nitrates, linking microdroplet chemistry to atmospheric, environmental, and geochemical processes. A third strand centers on materials chemistry, where droplets act as transient reactors for the formation of nanoparticles, nanostructures, and solid-phase materials, driven by coupled evaporation, charge and interfacial stresses, and mechanical deformation, connecting microdroplet phenomena to solid-state chemistry, rock weathering, soil formation, and environmental catalysis. These domains and their varied manifestations and interconnections are evident in the literature and will be discussed sequentially.

The iliopsoas muscle is important functionally and is often encountered during various surgical procedures. During routine cadaveric dissection, a previously undescribed muscular structure was located deep to the iliacus muscle and coursed inferomedially to merge with the posterior aspect of the psoas major, running over the iliopectineal bursa. A cadaveric study was performed to better understand this muscle attachment. Two hundred embalmed adult human cadaver donor sides were dissected. The iliacus and psoas major muscles were carefully detached from their origins and reflected anteriorly to expose the iliac fossa. Particular attention was directed toward identifying accessory muscular slips, distinct structures from the medial aspect of the iliac fossa. When present, the accessory muscles were documented according to presence and anatomy. Selected measurements were made of the muscle and its distance to surrounding structures. Histological analysis of the muscle was performed. An accessory attachment of the psoas major was identified in 28% of sides. This structure was located deep to the iliacus and distinct from the more laterally placed iliocapsularis muscle, although the two muscles traveled in the same plane. This muscle's vertically oriented fibers were lateral to the anterior inferior iliac spine, the lumbosacral trunk, and the obturator nerve. The iliopectineal arch gave rise to this muscle's medial attachment. Its origin was from the medial aspect of the ilium near the arcuate line. The muscle had a variable length, but distally, always attached to the deep surface of the psoas major, usually near the level of the inguinal ligament. Its mean length and width were 83.4 mm (52-100.4 mm, SD 10.4) and 11.3 mm (9.6-15.4 mm, SD 3.1), respectively. Histologically, the iliac attachment of the psoas major was composed of typical skeletal muscle fibers that had an intimate relationship to the underlying iliopectineal bursa. An accessory muscular structure located along the arcuate line of the ilium was identified in 28% of hemipelves examined. We have termed this muscle the iliac attachment of the psoas major. It demonstrated a consistent fascial separation from the overlying iliacus and merged with the psoas major inferiorly. Its anatomical position suggests a potential role in stabilizing the psoas major muscle and is related to the anterior hip joint. Further investigation using imaging correlation and biomechanical modeling is necessary to clarify its functional significance. Recognition of this variation may be relevant for surgeons treating hip pathology and for anatomists describing muscular variations of the iliac fossa. To our knowledge, this is the first report of an iliac attachment of the psoas major in humans.

Young adults aging out of foster care face a higher risk for negative adult outcomes, including homelessness, substance abuse, mental health issues, lower levels of education and employment, and financial instability, all of which affect their transition to adulthood. Extended foster care provides ongoing support to these young adults, yet participation remains low. This study explores associated factors, both demographic and foster care-related, of extended foster care participation. The study sample included 45,292 young adults in the United States who aged out of foster care between 2011 and 2020. A secondary analysis of merged data from the National Youth in Transition and the Adoption and Foster Care Analysis and Reporting System databases from 2011 to 2024 was conducted. A multivariable binomial logistic regression analysis examined whether demographic or foster care factors, including total time in foster care, age at first removal, placement setting type, and removal reasons, were associated with participation in extended foster care. Results indicate that total time in foster care, placement setting at age 17, and rural-urban context are most strongly associated with participation. Race and ethnicity, prior adoption, and the removal reasons of relinquishment, neglect, caretaker's inability to cope, and a child's disability, also show significant associations, though with smaller effect sizes. Implications for future research include the need for geographically focused studies that examine structural influences alongside individual factors and include the voices of those with lived experience.

Using a combination of Hubble Space Telescope and James Webb Space Telescope imaging, a runaway supermassive black hole was recently identified with an inferred velocity of 954_{-126}^{+110}  km s^{-1}, likely ejected from a compact star-forming galaxy at z≈0.96. Assuming the runaway black hole originated from a gravitational-wave-driven merger of two supermassive black holes (SMBHs), we combine its measured recoil velocity with gravitational-wave recoil predictions from numerical relativity and black-hole perturbation theory to constrain the mass ratio and spin configuration of the progenitor binary that overcame the final-parsec problem and merged ∼70  Myr ago. We find that the progenitor binary must have been precessing, with a mass ratio m_{1}/m_{2}≲6, and that the more massive SMBH likely possessed a high dimensionless spin magnitude (∼0.75) in order to generate a recoil of this magnitude. Such SMBH mergers could represent an interesting source population for the upcoming Laser Interferometer Space Antenna mission, with characteristic signal-to-noise ratios of order ≳10^{3}. Furthermore, the inferred progenitor SMBH properties suggest that the compact galaxy likely originated from a major, gas-rich ("wet") merger between two galaxies of comparable mass, with a mass ratio ≲4.

The estimation of postmortem interval (PMI) is a long-standing scientific challenge in forensic pathology, and its accuracy is susceptible to environmental conditions. Environmental temperature can affect processes such as corneal tissue dehydration, water migration, and degradation of biological macromolecules, altering the spectral evolution characteristics of the cornea after death. Therefore, the relationship between PMI and spectral features may change with temperature conditions. This study collected attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) spectra of 144 rat corneal homogenate samples under three controlled temperature conditions (4°C, 20°C, and 30°C) and six PMI time points (0, 6, 12, 24, 36, and 48 h). Firstly, partial least squares regression (PLSR) models were established for different temperature groups and combined data to compare the modellability of the PMI-spectral relationship under different temperature conditions; subsequently, variance-synchronized component analysis (ANOVA-simultaneous component analysis, ASCA) was used to decompose spectral variations into PMI, temperature, and PMI × temperature interaction effects, and dual screening was conducted using SHapley Additive exPlanations (SHAP) and other equivalent contribution analyses based on PLSR linear coefficients. The results showed that the single-temperature model was superior model performance to the combined, suggesting that temperature affects the relationship between PMI and spectra. The ASCA results indicated that PMI, temperature, and PMI × temperature interaction effects explained 27.38%, 5.94%, and 21.57% of the total spectral variation, respectively. The higher interaction effect suggests that temperature does not merely introduce constant background differences but also alters the spectral evolution trajectory related to PMI. Based on this, this study constructed a dual-screening strategy integrating ASCA effect decomposition and SHAP contribution analysis to identify interpretable PMI-related spectral bands under temperature-dependent variations. Through this screening framework, 56 wave numbers were identified and merged into 9 consecutive candidate spectral bands. These spectral bands are mainly related to protein, amide region, carbonyl group and fingerprint region molecular changes, suggesting that they may reflect the molecular degradation process of the cornea after death. The above spectral bands exhibit strong PMI-related effects, relatively low temperature main effects, and relatively consistent cross-temperature change trends under the current controlled conditions. However, they should not be interpreted as completely independent biomarkers that are completely unaffected by temperature. The proposed ASCA-SHAP dual screening framework in this study can provide candidate spectral features for cross-temperature and interpretable corneal PMI models.

Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on less practical measures. This study developed and validated machine learning models to predict H&Y scores at 5 years post-123I-ioflupane single-photon emission computed tomography (SPECT) imaging, leveraging both a real-world dataset and a subset of the PPMI cohort. The goal was to assess the utility of routinely collected clinical and imaging data for prognostic modeling. Data from medical records and imaging were harmonized from 343 real-world patients and 134 PPMI patients, resulting in a merged dataset with 83 overlapping features. Random Forest and Gradient Boosting models were trained to predict 5-year H&Y scores using varying amounts of longitudinal data and imaging features. Models using 2 years of clinical follow-up data achieved the highest predictive accuracy. The most important predictors were early H&Y scores, gait symptom severity, and select imaging features. Machine learning models can predict 5-year H&Y scores in PD using real-world clinical data, but imaging features add limited prognostic value. This study demonstrated that implementing machine learning models, when using real-world data, did not significantly improve the already known gap between prognostic modeling and real-world implementation. Improvement of models is, however, a promising prospect and further studies are encouraged.

Cementitious sand and gravel (CSG) is commonly used in construction engineering; however, its mix proportion design is complex, and traditional physical experiments face limitations such as long cycles, high costs, and susceptibility to external factors when obtaining high-quality sample data. In this study, a foundational dataset was first acquired through physical experiments: 100 sets of CSG specimens with different mix proportions (cement content 40, 50, 60, 70 kg/m3; water-to-binder ratio 1.0, 1.2, 1.4; sand ratio 0.1, 0.2, 0.3, 0.4; fly ash content 20, 30, 40, 50 kg/m3) were prepared. After 28 days of standard curing, compressive strength and splitting tensile strength tests were conducted using a WAW-1000 electro-hydraulic servo universal testing machine, yielding 100 sets of real mechanical property data. The coefficients of variation for all test groups were below 10%, confirming the reliability and repeatability of the experimental data. On this basis, a data augmentation method based on Conditional Tabular Generative Adversarial Networks (CTGAN) is proposed. Through adversarial training between the generator and the discriminator, the model learns the multi-dimensional distribution characteristics of the original CSG data and generates 100 synthetic samples, which are then merged with the original data to expand the dataset to 200 samples. The quality of the synthetic data is evaluated using Wasserstein distance and correlation matrix heatmaps. Furthermore, a hybrid XGBoost-LSTM prediction model is proposed-XGBoost is used for feature construction to capture nonlinear interactions among mix proportion variables, and the constructed features are then fed into an LSTM network for sequential learning and regression prediction. The results show that the CTGAN-generated data are highly consistent with the original data in terms of kernel density distributions and variable correlations, with Wasserstein distance significantly superior to four comparative methods: Bootstrap, SMOTE, GaussianCopula, and TVAE. After augmentation, the XGBoost-LSTM model achieves a coefficient of determination (R2) of 0.9897 for compressive strength prediction (vs. 0.9793 before augmentation) and 0.9801 for splitting tensile strength (vs. 0.9882 before augmentation, a slight decrease). The mean absolute percentage errors (MAPE) are 4.49% and 4.11%, and the root mean square errors (RMSE) are 0.201 and 0.049, respectively; both error metrics are reduced compared with those before augmentation. Compared with baseline models including XGBoost, LSTM, Random Forest (RF), and Support Vector Regression (SVR), the XGBoost-LSTM model exhibits the best performance across all evaluation metrics, and Wilcoxon signed-rank tests confirm that the performance differences are statistically significant (p < 0.05). The proposed method of CTGAN-based data augmentation combined with the XGBoost-LSTM hybrid model provides an effective solution to the problem of insufficient CSG sample data and offers a reference for data enhancement and performance prediction of other small-sample materials.

Geriatric syndromes (GS), such as falls, dementia, delirium and malnutrition, are complex clinical conditions affecting older adults which involve multiple organ systems and have major impact on quality of life and care. GS cut across disease categories, and are poorly represented in structured electronic health records. Natural language processing (NLP) offers an opportunity to extract valuable GS-related information from unstructured clinical text, such as hospital discharge summaries. However, the lack of high-quality annotated datasets limits the effectiveness of NLP models in this domain. This study introduces a manually annotated corpus designed for GS detection, enabling more accurate identification and classification of GS. We developed a comprehensive and detailed annotation scheme to label 12 common GS from hospital discharge summaries, incorporating key attributes such as diagnosis type, negation and event occurrence. The corpus consists of 2,040 manually annotated discharge summaries from National Health Service (NHS) Lothian hospitals in Scotland. To assess the effectiveness of NLP in extracting GS, we experimented with multiple pretrained transformer-based models, including base BERT (general-domain), BioBERT (biomedical-domain), BioClinicalBERT (clinical-domain) and BERT-cased (the cased English BERT checkpoint). The models were fine-tuned and tested for two types of tasks: named entity recognition (NER) and document-level labelling. We also considered an extra task of detecting contextual information with each GS mention (e.g., history, suspected, in-hospital). When context information is considered, two new tasks are called NER-C and DL-C, for NER and document-level labelling with context respectively. Our evaluation showed that, for the document-level labelling task, BERT-cased achieved the highest F1-score (0.897) and BioClinicalBERT performed best when negation was considered (F1-score: 0.888). For the NER task, BioClinicalBERT and BERT-cased achieved an F1-score of 0.883. Frailty (F1 = 1.0), Falls (F1 = 0.973) and Delirium (F1 = 0.946) are the GS entities with the best performing results. For NER-C, BERT-cased achieved the best F1 of 0.692 and BioBERT performed the worst (F1 = 0.658). In NER-C, the best results were achieved for context-aware falls and frailty labels, particularly when the syndrome was implied rather than explicitly stated. Document-level aggregation helped reduce inconsistencies, but the NER experiments used a flattened CoNLL-compatible representation of the original annotations, in which discontinuous mentions were converted into shortest covering spans and overlapping mentions were merged. Therefore, the reported NER results should be interpreted as baseline performance on a simplified representation of these structures, while low-frequency GS categories and sparse contextual labels also negatively affected model accuracy. This study demonstrates the effectiveness of NLP for extracting geriatric syndromes from unstructured clinical text and introduces a manually annotated corpus with detailed guidelines to support this task. The results also show that model performance is strongly shaped by dataset characteristics. More frequent and lexically clearer syndromes, such as frailty, falls and delirium, achieved the strongest results, whereas rarer categories and low-frequency attribute combinations, such as suspected, referral and some negated or context-specific labels, were harder to learn and yielded lower and less stable scores. Likewise, fine-grained annotation was more challenging than coarse-grained annotation because it increases label sparsity and requires the model to distinguish subtle contextual differences, such as current vs. historical mentions, implicit mentions and in-hospital onset. Entity-level extraction was further affected by discontinuous and overlapping mentions, which are common in clinical narratives and make boundary detection harder, whereas document-level aggregation reduced the impact of these local errors and therefore produced higher scores. These findings underline that data distribution, annotation complexity and mention structure directly influence model performance, and should be central considerations in future work on geriatric syndrome extraction.

Provisional stenting has become the default treatment strategy for most coronary bifurcation lesions However, in some clinical and anatomical settings, final two-stent implantation may yield better cardiovascular outcomes. This study aimed to identify preprocedural clinical and angiographic predictors associated with greater benefit from final two-stent implantation in coronary artery bifurcation lesions and to guide patient selection for this strategy. We analyzed 5333 patients with coronary bifurcation lesions (mean age 66.2&#xa0;years; 76% male) from the BIFURCAT registry, an international merged dataset combining the COBIS III and RAIN registries. All patients received second-generation drug-eluting stents; 82% underwent final single-stent and 18% final two-stent implantation. The primary endpoint was major adverse cardiac events (MACE)-a composite of all-cause death, myocardial infarction, and target lesion revascularization-at 2&#xa0;years. Multivariable Cox regression with interaction testing was used to identify preprocedural clinical and angiographic predictors of differential benefit from two-stent implantation. Six predictors demonstrated a statistically significant interaction with final one stent and two stent implantation groups: diabetes mellitus, main vessel reference diameter&#xa0;>&#xa0;3.0&#xa0;mm, main vessel lesion length&#xa0;<&#xa0;20&#xa0;mm, side branch lesion length&#xa0;&#x2265;&#xa0;20&#xa0;mm, non-left main lesion, and severe coronary artery calcification. Each variable was assigned one point to construct the Bifurcation Two-Stent (BTS) score. In patients with a BTS score&#xa0;<&#xa0;4 (80% of the cohort), final two-stent implantation was associated with significantly higher MACE rates compared with single-stent implantation (HR: 2.04; 95% CI: 1.64-2.56; P&#xa0;<&#xa0;0.001). Conversely, patients with a BTS score&#xa0;&#x2265;&#xa0;4 (approximately 20% of the cohort) demonstrated significantly lower MACE with two-stent implantation (HR: 0.56; 95% CI: 0.35-0.89; P&#xa0;=&#xa0;0.014), driven primarily by a reduction in hard endpoints including death and myocardial infarction. These findings remained consistent after adjustment for procedural variables and antiplatelet therapy. The BTS score may help identify patients with coronary bifurcation lesions who are most likely to benefit from final two-stent implantation. In patients with BTS score&#xa0;&#x2265;&#xa0;4, final two-stent implantation was associated with lower MACE and hard clinical endpoints, supporting a selective rather than routine two-stent strategy in bifurcation PCI.

Hospital delivery volume has been linked to severe maternal morbidity (SMM), which increases medical costs, readmissions, hospital stays, and mortality risk. During Fiscal Years&#xa0;(FY) 2019-2023, the Military Health System (MHS) rolled out a new electronic health record, merged facilities under Defense Health Agency (DHA) management, and weathered the COVID-19 pandemic. This study aimed to examine the association between delivery volume and odds of SMM and 30-day readmissions for deliveries at U.S. military hospitals for FY&#xa0;2019-2023. This cross-sectional study analyzed healthcare claims data from MHS beneficiary women aged 15-54 years delivering FY&#xa0;2019-2023. Delivery volume was measured using International Classification of Diseases-10 (ICD-10) and Medicare Severity-Diagnosis Related Group codes. Military hospitals were grouped into quartiles by volume over the study period. SMM was measured by 21 ICD-10 indicators (any/none). Any postpartum readmission (yes/no) to the same hospital within 30 days of discharge was included. Covariables included hospital identifier and maternal comorbidities and demographics associated with each delivery. Stepwise logistic regression was performed. Women delivering at hospitals in the lowest, lower-middle, and highest volume quartiles had significantly lower (19-30%) odds of SMM compared with the upper-middle quartile (p < 0.05). Those delivering at lower and lower-middle quartile hospitals had significantly higher (29-46%) odds of 30-day readmissions compared with the upper-middle quartile (p < 0.05), with no significant difference found among those in the highest quartile. Delivery volume was significantly associated with SMM and 30-day readmission odds among MHS deliveries FY&#xa0;2019-2023. This differs from previous FY&#xa0;2015-2018 findings, therefore concurrent effects of the DHA transition and COVID-19 pandemic cannot be ruled out.

Treating cartilage injuries has long been a complex challenge; however, recent progress has greatly increased the variety of effective therapeutic approaches. The minced autologous matrix-induced chondrogenesis plus procedure for full-thickness knee cartilage lesions uses a collagen matrix combined with shaver-minced cartilage graft and bone marrow aspirate. It merges a source of bone marrow-derived stem cells and a biological autologous cartilage scaffold, leveraging their chondrogenic differentiation capacity and paracrine activity. This technique aims to enhance outcomes and provides an associated single-stage knee cartilage repair option with biological augmentation.

Aptamers are single-stranded DNA or RNA molecules that exhibit remarkable affinity and specificity for a broad spectrum of biological targets, positioning them as compelling alternatives to antibodies in both diagnostics and therapeutics. Their ease of synthesis, chemical stability, and tunable binding properties make them highly adaptable for molecular recognition applications. Carbon Nanotubes (CNTs), on the other hand, are renowned for their unique structural, electrical, optical, and mechanical properties, ultimately providing an ideal nanoscale scaffold for aptamer conjugation. When combined with aptamers, CNTs form multifunctional hybrid nanoplatforms that merge the molecular selectivity of aptamers with the high surface area, conductivity, and mechanical strength of CNTs. This review discusses the fundamental concepts, functionalization strategies, and biomedical potential of aptamer-CNT hybrids. Both covalent and non-covalent conjugation approaches are examined, highlighting their impact on stability, sensitivity, and biocompatibility at the biointerface. Recent progress in molecular recognition, biosensing, targeted drug delivery, imaging, and theranostic applications is also summarized. Particular attention is given to the role of CNTs in enhancing electron transfer, signal amplification, and controlled therapeutic release. Moreover, diagnostic and therapeutic applications across various disease models, including cancer, infectious diseases, and neurodegenerative disorders, are highlighted. Furthermore, emerging challenges related to the toxicity, biodegradability, and pharmacokinetics of CNT-based hybrids are addressed, while considering regulatory and ethical perspectives that govern their clinical translation. Overall, aptamer-CNT hybrids hold immense promise as versatile, tunable, and multifunctional platforms that could fundamentally transform next-generation precision medicine and nanotherapeutic systems.

Here, we describe the merging of titanocene-catalyzed radical arylation with electro-organic synthesis that combines the advantages of both fields. The critical mechanistic issue of the titanocene-catalyzed radical arylation, the efficiency of rearomatization by a proton-coupled electron transfer, can be efficiently resolved via a radical polar crossover. This is achieved in a unique manner by consecutive paired galvanostatic electrolysis through a decoupling of the electron and proton transfer steps by spatially separating the critical redox processes, the anodic oxidation of the radical &#x3c3;-complex to the cationic &#x3c3;-complex, and the mild in situ preparation of the active catalyst by cathodic reduction of Cp2TiCl2.

Conventional surfactants excel at stabilizing nanoemulsions but remain therapeutically inert-they carry drugs but do not actively participate in therapy. An ideal surfactant should integrate delivery function with intrinsic bioactivity, yet such dual-purpose molecules are rare. Here, we transform vitamin C into a fluorinated surfactant (F-VC) via mild, short-chain fluorination, preserving its redox-active core while introducing amphiphilicity. The resulting molecule retains potent antioxidant capacity and exhibits pronounced surface activity, enabling the one-step fabrication of nanoemulsions with built-in antioxidant function-a paradigm shift from conventional inert carriers. In vitro, F-VC nanoemulsions outperform equimolar vitamin C by 43% in radical scavenging, achieve 89.4% wound closure in scratch assays, and kill up to 99% of Staphylococcus aureus. In a diabetic rat model, they drive 99% wound closure by day 14. Mechanistically, they suppress inflammatory cytokines (IL-6, TNF-&#x3b1;) while promoting angiogenesis markers (CD31, &#x3b1;-SMA). By merging therapeutic and delivery functions in a single molecular entity, F-VC simplifies formulation, efficiently encapsulates diverse oil-soluble bioactives, and establishes a new class of bioactive surfactants. This work establishes a new class of bioactive surfactants-transforming vitamin C into a functional nanomaterial at the nexus of materials science and biomedicine.

Understanding gene spatial expression and the organization of multicellular systems is vital for disease diagnosis and studying biological processes. However, existing models often struggle to integrate gene expression data with cellular spatial information effectively. Here we introduce SpatialFormer, a hybrid framework combining convolutional networks and transformers to learn single-cell multimodal and multiscale information in the niche context, including expression data and subcellular gene spatial distribution. Pretrained on 700&#x2009;million cell pairs from 17&#x2009;million spatially resolved single cells across 71 Xenium slides, SpatialFormer merges gene spatial expression profiles with cell niche information via the pairwise training strategy. Our findings demonstrate that SpatialFormer distills biological signals across various tasks, including single-cell batch correction, cell-type annotation and co-localization detection. The perturbation analysis identified gene pairs essential for the immune cell-cell communication in pulmonary fibrosis, epithelial-myoepithelial co-localization and tumor transition signals in breast cancer. These advancements enhance our understanding of cellular dynamics and offer additional pathways for applications in biomedical research.

Gliomas are the most prevalent primary brain tumors in adults, and murine modeling suggests that they may arise from oncogenic mutations in NSCs or early differentiating progenitors. Orthotopic transplantation models derived from mutant NSCs thus have the potential to yield valuable preclinical information about glioma pathogenesis, outcomes, and treatment response. These models require the introduction of an oncogenic mutation into NSCs, preferentially coupled with reporters to aid in the determination of transfection/transduction efficiency and to monitor tumor growth in vivo. This protocol outlines a dual transduction-based methodology for generating mutant NSCs suitable for in vivo tumor monitoring. In this technique, isolated NSCs are first transduced with lentiviruses that contain a gene of interest (oncogenic mutation or green fluorescent protein [GFP] control) in addition to a blasticidin-resistance element. Following blasticidin selection, cells are secondarily transduced with a bicistronic lentiviral vector expressing luciferase and red fluorescent protein (RFP), as well as a puromycin-resistance element. After puromycin selection, cells can be expanded for proliferation assays, orthotopic transplantation, or other downstream applications. This dual transduction approach allows for estimation of transduction efficiency at both infection stages as well as monitoring in vivo&#xa0;tumor establishment, growth, and response to therapy using noninvasive bioluminescent imaging (BLI). This system represents a useful tool for glioma research, merging the biological relevance of adult neural stem cell-derived tumor modeling with the practical benefit of real-time imaging capability, ultimately enhancing the understanding of glioma biology and the advancement of new therapeutic approaches.