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Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

Immune checkpoint inhibitors (ICIs) have transformed oncology, yet durable benefit remains confined to a minority of patients, revealing the limitations of single biomarkers such as PD-L1 expression, tumor mutational burden, and microsatellite instability. Multi-omics profiling, spanning genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiomics, and imaging-derived radiomics/pathomics, enables a systems-level interrogation of tumor–immune interactions. It captures lineage plasticity, antigen-presentation defects, metabolic and epigenetic suppression, stromal remodeling, and microbiome-driven immune tone that collectively shape ICI sensitivity and resistance. Artificial intelligence (AI) and machine learning are increasingly indispensable for fusing these heterogeneous, high-dimensional data into deployable composite predictors and mechanistically grounded signatures, while explainability approaches (e.g., SHAP, Grad-CAM) help link model outputs to actionable biology. This review synthesizes emerging AI-enabled multi-omics biomarkers across major tumor types, highlights clinical applications in response stratification, combination-therapy selection, and longitudinal monitoring, and discusses key translational barriers, including cohort and platform heterogeneity, limited prospective validation, privacy constraints, model drift, and equity. We conclude by outlining future directions in single-cell and spatial multi-omics integration, federated learning, and generative modeling to accelerate robust, generalizable precision immunotherapy. Pragmatic implementation will require harmonized pre-analytics, clinically feasible assays or distilled panels, and decision-support interfaces that communicate calibrated uncertainty to oncologists.

Radiation enteritis remains a major dose-limiting toxicity in cervical cancer radiotherapy, significantly impairing treatment continuity, long-term gastrointestinal function, and patient quality of life. Despite advances in radiation techniques, the biological heterogeneity of intestinal radiosensitivity continues to challenge effective prevention and management. This review synthesizes current evidence on the core mechanistic axes underlying radiation enteritis, with a focus on DNA damage response failure, oxidative stress amplification, immune dysregulation, and microbiota disruption. We further summarize emerging predictive frameworks integrating clinical variables, dosimetric parameters, radiomics, and circulating biomarkers to enable individualized risk stratification. Particular attention is given to translational therapeutic strategies, including antioxidant pathway modulation, inflammasome targeting, microbiota engineering, and tissue-protective agents, highlighting both their mechanistic rationale and clinical feasibility. By linking molecular pathophysiology with predictive modeling and intervention development, this review provides an integrated roadmap for precision prevention and management of radiation enteritis in cervical cancer radiotherapy. Such a framework may facilitate risk-adapted treatment planning, mitigate gastrointestinal toxicity, and ultimately improve therapeutic outcomes.

Surveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children. This cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations. We developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses. The combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.

Good stability and controlled drug release are important properties of polymeric micelles for drug delivery. A good candidate for drug delivery must have outstanding stability in a normal physiological environment, followed with low drug leakage and side effects. Moreover, the chemotherapeutic drug in the micellar core should also be quickly and “on-demand” released in the intracellular microenvironment at the tumor site, which is in favor of overcoming multidrug resistance (MDR) effects of tumor cells. In this work, a mixed micelle was prepared by the simple mix of two amphiphilic copolymers, namely PCL-SS-P(PEGMA-co-MAEBA) and PCL-SS-PDMAEMA, in aqueous solution. In the mixed micelle’s core–shell structure, PCL blocks were used as the hydrophobic core, while the micellar hydrophilic shell consisted of two blocks, namely P(PEGMA-co-MAEBA) and PDMAEMA. In the micellar shell, PEGMA provided hydrophilicity and stability, while MAEBA introduced the aldehyde sites for reversible crosslinking. Meanwhile, the PDMAEMA blocks were also introduced in the micellar shell for pH-responding protonation and swelling of the micelle. The disulfide bonds between the hydrophobic core and hydrophilic shell had redox sensitive properties. Reversible cross-linked micelles (RCLMs) were obtained by crosslinking the micellar shell with an imine structure. RCLMs showed good stability and excellent ability against extensive dilution by aqueous solution. In addition, the stability in different conditions with various pH values and glutathione (GSH) concentrations was studied. Then, the anticancer drug doxorubicin (DOX) was selected as the model drug to evaluate drug entrapment and release capacity of mixed micelles. The in vitro release profiles indicated that this RCLM had controlled drug release. In the simulated normal physiological environment (pH 7.4), the drug release of the RCLMs was restrained obviously, and the cumulative drug release content was only 25.7 during 72 h. When it came to acidic conditions (pH 5.0), de-crosslinking of the micelles occurred, as well as protonation of PDMAEMA blocks and micellar swelling at the same time, which enhanced the drug release to a large extent (81.4%, 72 h). Moreover, the drug release content was promoted further in the presence of the reductant GSH. In the condition of pH 5.0 with 10 mM GSH, disulfide bonds broke-up between the micelle core and shell, followed by shedding of the shell from the inner core. Then, the micellar disassembly (degradation) happened based on the de-crosslinking and swelling, and the drug release was as high as 95.3%. The MTT assay indicated that the CLSMs showed low cytotoxicity and good biocompatibility against the HepG2 cells. In contrast, the DOX-loaded CLSMs could efficiently restrain the proliferation of tumor cells, and the cell viability after 48 h incubation was just 13.2%, which was close to that of free DOX. This reversible cross-linked mixed micelle with pH/redox responsive behaviors is a potential nanocarrier for chemotherapy.

Purpose Children and adolescents are vulnerable to dental problems and oral diseases. This paper presents the development of two multi-item self-report scales for use in assessing oral health status of children and adolescents. Methods Following the Patient-Reported Outcome Measurement Information System framework, survey questions were designed using a newly developed conceptual model. These items were administered to 334 children and adolescents (8–17 years) along with concurrent dental exams. Exploratory and confirmatory factor analyses were conducted and the item response theory graded response model was used to estimate item parameters and oral health status scores and to identify short-form items. The items were selected by high level of information and wide coverage of different domains to assess Child Oral Health Status Index (COHSI) and treatment referral recommendations (RR). Results The long form consists of 28 items. The short-form includes 12 items (8 for COHSI and 7 for RR with 3 common items).The intra-class correlations between long form and short-form were 0.90 for COHSI and 0.87 for RR. Conclusion The short-forms provide a possible solution for the longstanding challenge of oral health evaluation for large populations of children and adolescents. The calibrated long form provides the foundation for computer adaptive test administration. These oral health assessment toolkits can be used for oral health screening, surveillance program, policy planning, and research.

It is of great significance to study the structure property and self-assembly of amphiphilic block copolymer in order to effectively and efficiently design and prepare drug delivery systems. In this work, dissipative particle dynamics (DPD) simulation method was used to investigate the structure property and self-assembly ability of pH-responsive amphiphilic block copolymer poly(methyl methacrylate-co-methacrylic acid)-b-poly(aminoethyl methacrylate) (poly(MMA-co-MAA)-b-PAEMA). The effects of different block ratios (hydrophilic PAEMA segment and pH-sensitive PMAA segment) in copolymer on self-assembly and drug loading capacity including drug distribution were extensively investigated. The increase of hydrophilic PAEMA facilitated the formation of a typical core-shell structure as well as a hydrophobic PMAA segment. Furthermore, the optimal drug-carrier ratio was confirmed by an analysis of the drug distribution during the self-assembly process of block copolymer and model drug Ibuprofen (IBU). In addition, the drug distribution and nanostructure of IBU-loaded polymeric micelles (PMs) self-assembled from precise block copolymer (PMMA-b-PMAA-b-PAEMA) and block copolymer (poly(MMA-co-MAA)-b-PAEMA) with random pH-responsive/hydrophobic structure were evaluated, showing that almost all drug molecules were encapsulated into a core for a random copolymer compared to the analogue. The nanostructures of IBU-loaded PMs at different pH values were evaluated. The results displayed that the nanostructure was stable at pH < pKa and anomalous at pH > pKa which indicated drug release, suggesting that the PMs could be used in oral drug delivery. These findings proved that the amphiphilic block copolymer P(MMA30-co-MAA33)-b-PAEMA38 with random structure and pH-sensitivity might be a potential drug carrier. Moreover, DPD simulation shows potential to study the structure property of PMs self-assembled from amphiphilic block copolymer.

Background This scoping review reports on studies that collect survey data using quantitative research to measure self-reported oral health status outcome measures. The objective of this review is to categorize measures used to evaluate self-reported oral health status and oral health quality of life used in surveys of general populations. Methods The review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) with the search on four online bibliographic databases. The criteria include (1) peer-reviewed articles, (2) papers published between 2011 and 2021, (3) only studies using quantitative methods, and (4) containing outcome measures of self-assessed oral health status, and/or oral health-related quality of life. All survey data collection methods are assessed and papers whose methods employ newer technological approaches are also identified. Results Of the 2981 unduplicated papers, 239 meet the eligibility criteria. Half of the papers use impact scores such as the OHIP-14; 10% use functional measures, such as the GOHAI, and 26% use two or more measures while 8% use rating scales of oral health status. The review identifies four data collection methods: in-person, mail-in, Internet-based, and telephone surveys. Most (86%) employ in-person surveys, and 39% are conducted in Asia-Pacific and Middle East countries with 8% in North America. Sixty-six percent of the studies recruit participants directly from clinics and schools, where the surveys were carried out. The top three sampling methods are convenience sampling (52%), simple random sampling (12%), and stratified sampling (12%). Among the four data collection methods, in-person surveys have the highest response rate (91%), while the lowest response rate occurs in Internet-based surveys (37%). Telephone surveys are used to cover a wider population compared to other data collection methods. There are two noteworthy approaches: 1) sample selection where researchers employ different platforms to access subjects, and 2) mode of interaction with subjects, with the use of computers to collect self-reported data. Conclusion The study provides an assessment of oral health outcome measures, including subject-reported oral health status and notes newly emerging computer technological approaches recently used in surveys conducted on general populations. These newer applications, though rarely used, hold promise for both researchers and the various populations that use or need oral health care.

Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using a machine learning algorithm applied to parent perceptions of their child’s oral health assessed by survey. Methods: The sample consisted of 182 parents/caregivers and their children 2–7 years of age living in Los Angeles County. Random forest (a machine learning algorithm) was used to identify survey items that were predictors of active caries and caries experience. We applied a three-fold cross-validation method. A threshold was determined by maximizing the sum of sensitivity and specificity conditional on the sensitivity of at least 70%. The importance of survey items to classifying active caries and caries experience was measured using mean decreased Gini (MDG) and mean decreased accuracy (MDA) coefficients. Results: Survey items that were strong predictors of active caries included parent’s age (MDG = 0.84; MDA = 1.97), unmet needs (MDG = 0.71; MDA = 2.06) and the child being African American (MDG = 0.38; MDA = 1.92). Survey items that were strong predictors of caries experience included parent’s age (MDG = 2.97; MDA = 4.74), child had an oral health problem in the past 12 months (MDG = 2.20; MDA = 4.04) and child had a tooth that hurt (MDG = 1.65; MDA = 3.84). Conclusion: Our findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents.

This research aimed to explore how Strychnine (Str) ion-pair compounds affect the in vitro transdermal process. In order to prevent the influence of different functional groups on skin permeation, seven homologous fatty acids were selected to form ion-pair compounds with Str. The in vitro permeation fluxes of the Str ion-pair compounds were 2.2 to 8.4 times that of Str, and Str-C10 had the highest permeation fluxes of 42.79 ± 19.86 µg/cm2/h. The hydrogen bond of the Str ion-pair compounds was also confirmed by Fourier Transform Infrared (FTIR) Spectroscopy, Nuclear Magnetic Resonance (NMR) Spectroscopy and molecular simulation. In the process of molecular simulation, the intercellular lipid and the viable skin were represented by ceramide, cholesterol and free fatty acid of equal molar ratios and water, respectively. It was found by the binding energy curve that the Str ion-pair compounds had better compatibility with the intercellular lipid and water than Str, which indicated that the affinity of Str ion-pair compounds and skin was better than that of Str and skin. Therefore, it was concluded that Str ion-pair compounds can be distributed from the vehicle to the intercellular lipid and viable skin more easily than Str. These findings broadened our knowledge about how Str ion-pair compounds affect the transdermal process.