RNA Splicing Events in Circulation Distinguish Individuals with and without New-Onset Type 1 Diabetes.

CONTEXT: Alterations in RNA splicing may influence protein isoform diversity that contributes to or reflects the pathophysiology of certain diseases. Whereas specific RNA splicing events in pancreatic islets have been investigated in models of inflammation in vitro, how RNA splicing in the circulation correlates with or is reflective of T1D disease pathophysiology in humans remains unexplored. OBJECTIVE: To use machine learning to investigate if alternative RNA splicing events differ between individuals with and without new-onset type 1 diabetes (T1D) and to determine if these splicing events provide insight into T1D pathophysiology. METHODS: RNA deep sequencing was performed on whole blood samples from two independent cohorts: a training cohort consisting of 12 individuals with new-onset T1D and 12 age- and sex-matched nondiabetic controls and a validation cohort of the same size and demographics. Machine learning analysis was used to identify specific isoforms that could distinguish individuals with T1D from controls. RESULTS: Distinct patterns of RNA splicing differentiated participants with T1D from unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects. CONCLUSIONS: Alternative RNA splicing events in whole blood are significantly enriched in individuals with new-onset T1D and can effectively distinguish these individuals from unaffected controls. Our findings also suggest that RNA splicing profiles offer the potential to provide insights into disease pathogenesis.

Synergism between remdesivir and ribavirin leads to SARS-CoV-2 extinction in cell culture.

BACKGROUND AND PURPOSE: There is a need for effective anti-COVID-19 treatments, mainly for individuals at risk of severe disease such as the elderly and the immunosuppressed. Drug repositioning has proved effective in identifying drugs that can find a new application for the control of coronavirus disease, in particular COVID-19. The purpose of the present study was to find synergistic antiviral combinations for COVID-19 based on lethal mutagenesis. EXPERIMENTAL APPROACH: The effect of combinations of remdesivir and ribavirin on the infectivity of SARS-CoV-2 in cell culture has been tested. Viral populations were monitored by ultra-deep sequencing, and the decrease of infectivity as a result of the treatment was measured. KEY RESULTS: Remdesivir and ribavirin exerted a synergistic inhibitory activity against SARS-CoV-2, quantified both by CompuSyn (Chou-Talalay method) and Synergy Finder (ZIP-score model). In serial passage experiments, virus extinction was readily achieved with remdesivir-ribavirin combinations at concentrations well below their cytotoxic 50 value, but not with the drugs used individually. Deep sequencing of treated viral populations showed that remdesivir, ribavirin, and their combinations evoked significant increases of the number of viral mutations and haplotypes, as well as modification of diversity indices that characterize viral quasi-species. CONCLUSION AND IMPLICATIONS: SARS-CoV-2 extinction can be achieved by synergistic combination treatments based on lethal mutagenesis. In addition, the results offer prospects of triple drug treatments for effective SARS-CoV-2 suppression.

Assessment of risk-adjusted mortality ratio (RAMR) in bloodstream infections using all-patient refined diagnosis-related groups (APR-DRGs).

OBJECTIVES: To calculate a risk-adjusted mortality ratio (RAMR) for bloodstream infections (BSIs) using all-patient refined diagnosis-related groups (APR-DRGs) and compare it with the crude mortality rate (CMR). METHODS: Retrospective observational study of prevalent BSI at our institution from January 2019 to December 2022. In-hospital mortality was adjusted with a binary logistic regression model adjusting for sex, age, admission type and mortality risk for the hospitalization episode according to the four severity levels of APR DRGs. The RAMR was calculated as the ratio of observed to expected in-hospital mortality, and the CMR was calculated as the proportion of deaths among all bacteraemia episodes. RESULTS: Of 2939 BSIs, 2541 were included: Escherichia coli (n = 1310), Klebsiella pneumoniae (n = 428), Pseudomonas aeruginosa (n = 209), Staphylococcus aureus (n = 498) and candidaemia (n = 96). A total of 436 (17.2%) patients died during hospitalization and 279 died within the first 14 days after the onset of BSI. Throughout the period, all BSI cases had a mortality rate above the expected adjusted mortality (RAMR value greater than 1), except for Escherichia coli (1.03; 95% CI 0.86-1.21). The highest overall RAMR values were observed for P. aeruginosa, Candida and S. aureus with 2.06 (95% CI 1.57-2.62), 1.99 (95% CI 1.3-2.81) and 1.8 (95% CI 1.47-2.16), respectively. The temporal evolution of CMR may differ from RAMR, especially in E. coli, where it was reversed. CONCLUSIONS: RAMR showed higher than expected mortality for all BSIs studied except E. coli and provides complementary to and more clinically comprehensive information than CMR, the currently recommended antibiotic stewardship programme mortality indicator.

In Vitro Antimycotic Activity and Structural Damage against Canine Malassezia pachydermatis Strains Caused by Mexican Stingless Bee Propolis.

This work describes the antimycotic activity of propolis from the stingless bees Scaptotrigona mexicana and Tetragonisca angustula, collected from two Mexican regions (Veracruz and Chiapas, respectively), against three clinical isolates and the reference strain ATCC 14522 of Malassezia pachydermatis, the causative agent of canine otitis. The chemical components of the ethanolic extracts of propolis were determined by gas chromatography coupled with mass spectrometry (GC-MS), and sesquiterpenes were the predominant compounds. The antimycotic activity was evaluated by plate microdilution. The induced changes in the yeasts were evaluated by fluorescence microscopy and staining with calcofluor white and propidium iodide. The minimum inhibitory concentration (MIC) was 7.11 mg/mL, and the minimum fungicidal concentration was 21.33 mg/mL for both extracts. The EPPs of Scaptotrigona mexicana and Tetragonisca angustula caused substantial damage to yeast morphology, where the propidium iodide staining of the yeasts treated with both EEPs revealed the penetration of this marker, which indicates the destruction of the cell wall and plasma membrane of the fungi. This result suggests that these types of propolis could be used as alternative treatments for canine external otitis. To the best of our knowledge, this seems to be the first scientific report that has demonstrated structural damage in Malassezia pachydermatis by Mexican stingless bee propolis.

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes.

Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic ß cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies-biomarkers of autoimmunity-is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through disease onset. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D have lower complement protein levels relative to those who do not progress within a similar time frame. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.

Incipient functional SARS-CoV-2 diversification identified through neural network haplotype maps.

Since its introduction in the human population, SARS-CoV-2 has evolved into multiple clades, but the events in its intrahost diversification are not well understood. Here, we compare three-dimensional (3D) self-organized neural haplotype maps (SOMs) of SARS-CoV-2 from thirty individual nasopharyngeal diagnostic samples obtained within a 19-day interval in Madrid (Spain), at the time of transition between clades 19 and 20. SOMs have been trained with the haplotype repertoire present in the mutant spectra of the nsp12- and spike (S)-coding regions. Each SOM consisted of a dominant neuron (displaying the maximum frequency), surrounded by a low-frequency neuron cloud. The sequence of the master (dominant) neuron was either identical to that of the reference Wuhan-Hu-1 genome or differed from it at one nucleotide position. Six different deviant haplotype sequences were identified among the master neurons. Some of the substitutions in the neural clouds affected critical sites of the nsp12-nsp8-nsp7 polymerase complex and resulted in altered kinetics of RNA synthesis in an in vitro primer extension assay. Thus, the analysis has identified mutations that are relevant to modification of viral RNA synthesis, present in the mutant clouds of SARS-CoV-2 quasispecies. These mutations most likely occurred during intrahost diversification in several COVID-19 patients, during an initial stage of the pandemic, and within a brief time period.

Design of Two New Sulfur Derivatives of Perezone: In Silico Study Simulation Targeting PARP-1 and In Vitro Study Validation Using Cancer Cell Lines.

Poly-ADP-Ribose Polymerase (PARP-1) is an overexpressed enzyme in several carcinomas; consequently, the design of PARP-1 inhibitors has acquired special attention. Hence, in the present study, three compounds (8-10) were produced through a Michael addition protocol, using phenylmethanethiol, 5-fluoro-2-mercaptobenzyl alcohol, and 4-mercaptophenylacetic acid, respectively, as nucleophiles and perezone as the substrate, expecting them to be convenient candidates that inhibit PARP-1. It is convenient to note that in the first stage of the whole study, the molecular dynamics (MD) simulations and the quantum chemistry studies of four secondary metabolites, i.e., perezone (1), perezone angelate (2), hydroxyperezone (3), and hydroxyperezone monoangelate (4), were performed, to investigate their interactions in the active site of PARP-1. Complementarily, a docking study of a set of eleven sulfur derivatives of perezone (5-15) was projected to explore novel compounds, with remarkable affinity to PARP-1. The molecules 8-10 provided the most adequate results; therefore, they were evaluated in vitro to determine their activity towards PARP-1, with 9 having the best IC50 (0.317 µM) value. Additionally, theoretical calculations were carried out using the density functional theory (DFT) with the hybrid method B3LYP with a set of base functions 6-311++G(d,p), and the reactivity properties were compared between the natural derivatives of perezone and the three synthesized compounds, and the obtained results exhibited that 9 has the best properties to bind with PARP-1. Finally, it is important to mention that 9 displays significant inhibitory activity against MDA-MB-231 and MCF-7 cells, i.e., 145.01 and 83.17 µM, respectively.

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification.

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular snapshot" is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

Decrease in multiple complement protein levels is associated with the development of islet autoimmunity and type 1 diabetes.

Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic ß-cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies - biomarkers of autoimmunity - is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through disease onset. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D have lower complement protein levels relative to those who do not progress within a similar timeframe. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.

Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification.

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography-mass spectrometry (GC-MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics.

The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g., retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exist, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size; however FDR estimation error typically worsens as the set of reference compounds diminishes.

Missing data in multi-omics integration: Recent advances through artificial intelligence.

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

Impact of COVID-19 on tuberculosis detection and treatment in Baja California, México.

In 2020, Mexico reported the lowest tuberculosis (TB) incidence on record, and it is unclear to what extent COVID-19 has impacted TB surveillance, diagnosis, and treatment. It is important to understand COVID-19's impact in Baja California (BC), which has the highest TB burden in Mexico. With the increasing number of migrants and asylum seekers arriving in BC, limited resources and crowded living conditions increase the risk of TB transmission. The purpose of this study was to assess the impact of COVID-19 on TB diagnosis and treatment in BC. We were also interested in health disparities experienced by migrants in BC. We conducted a mixed methods analysis using quantitative surveillance data obtained from the Mexico National TB Program (NTP) and qualitative data collected through in-depth interviews and focus group discussions with TB program directors and personnel in BC's four provincial health jurisdictions. Compared to the year prior, surveillance data from March 2020 - February 2021 revealed that TB incidence in BC declined by 30.9% and favorable TB outcomes (TB cure or treatment completion) declined by 49.8%. Elucidating differences by migrant status was complicated by the lack of standardized collection of migrant status by the NTP. Qualitative analysis revealed that TB diagnostic and treatment supplies and services became limited and disproportionately accessible across jurisdictions since the pandemic began; however, favorable adaptations were also reported, such as increased telemedicine use and streamlined care referral processes. Participants shared that migrant status is susceptible to misclassification and that TB care is difficult due to the transitory nature of migrants. This study did not identify major differences in TB service delivery or access between migrants and non-migrants in BC; however, migrant status was frequently missing. COVID-19 has overwhelmed health systems worldwide, disrupting timely TB diagnostic and treatment services, and potentially caused underdiagnosis of TB in BC. TB programs in BC should quickly restore essential services that were disrupted by COVID-19 while identifying and preserving beneficial program adaptations, such as telemedicine and streamlined care referral processes. Improved methods for documenting migrant status of TB cases are also needed.

Single-Unit Short Implants in the Molar Region: A Retrospective Study with a Minimum 3-Year Follow-up.

The high biomechanical loads in molar region wounds challenge the indication for short implants to be used as a single-unit implant. This study reports on the outcomes of single-unit short implants (≤ 8.0 mm) in the maxillary and mandibular molar regions. Forty-nine short implants were placed in 48 patients to replace a missing molar tooth. Two-piece restorations with screw retention were fabricated. During the follow-up, implant survival and marginal bone loss (MBL) were assessed. The known implant length was used as a reference to calibrate the linear measurements on digital periapical radiographs, and descriptive statistical analysis was performed. The implants were followed over a period of 47 ± 12 months. No implant failure was recorded, and no prosthesis failure was observed. The average MBL was 0.15 ± 0.5 mm. The mean crown height space was 13 ± 3 mm. The overall crown-to-implant ratio was 1.7 ± 0.4. Two technical complications occurred due to the loosening of the unit abutment. After screw re-tightening, no more screw loosening was observed. This study supports the use of short implants as a single-unit implant in the maxillary and mandibular molar regions.

The human bile salt sodium deoxycholate induces metabolic and cell envelope changes in Salmonella Typhi leading to bile resistance.

Introduction. Salmonella enterica serovar Typhi (S. Typhi) is the etiological agent of typhoid fever. To establish an infection in the human host, this pathogen must survive the presence of bile salts in the gut and gallbladder.Hypothesis. S. Typhi uses multiple genetic elements to resist the presence of human bile.Aims. To determine the genetic elements that S. Typhi utilizes to tolerate the human bile salt sodium deoxycholate.Methodology. A collection of S. Typhi mutant strains was evaluated for their ability to growth in the presence of sodium deoxycholate and ox-bile. Additionally, transcriptomic and proteomic responses elicited by sodium deoxycholate on S. Typhi cultures were also analysed.Results. Multiple transcriptional factors and some of their dependent genes involved in central metabolism, as well as in cell envelope, are required for deoxycholate resistance.Conclusion. These findings suggest that metabolic adaptation to bile is focused on enhancing energy production to sustain synthesis of cell envelope components exposed to damage by bile salts.

Environmental conditions drive zooplankton community structure in the epipelagic oceanic water of the southern Gulf of Mexico: A molecular approach.

Zooplankton plays a pivotal role in sustaining the majority of marine ecosystems. The distribution patterns and diversity of zooplankton provide key information for understanding the functioning of these ecosystems. Nevertheless, due to the numerous cryptic and sibling species and the lack of diagnostic characteristics for early developmental stages, the identification of the global-to-local patterns of zooplankton biodiversity and biogeography remains challenging in different research fields. The spatial and temporal changes in the zooplankton community in the open waters of the southern Gulf of Mexico were assessed using metabarcoding analysis of the V9 region of 18S rRNA and mitochondrial cytochrome oxidase c subunit I (COI). Additionally, a multiscale analysis was implemented to evaluate which environmental predictors may explain the variability in the structure of the zooplankton community. Our findings suggest that the synergistic effects of dissolved oxygen concentration, temperature, and longitude (intended as a proxy for still unidentified predictors) may explain both spatial and temporal zooplankton variability even with low contribution. Furthermore, the zooplankton distribution probably reflects the coexistence of three heterogeneous ecoregions and a bio-physical partitioning of the studied area. Finally, some taxa were either exclusive or predominant with either 18S or COI markers. This may suggest that comprehensive assessments of the zooplankton community may be more accurately met by the use of multilocus approaches.

Alcoholic Liver Disease Among Patients with Wernicke Encephalopathy: A Multicenter Observational Study.

BACKGROUND: data regarding the association between Wernicke encephalopathy (WE) and alcoholic liver disease (ALD) are scarce in spite of alcohol consumption being the main risk factor for WE. AIMS: to describe the frequency of ALD in a cohort of patients diagnosed with WE and alcohol use disorders (AUDs) and to compare the characteristics of WE patients with and without ALD. METHODS: we conducted an observational study in 21 centers through a nationwide registry of the Spanish Society of Internal Medicine. WE Caine criteria were applied and demographic, clinical, and outcome variables were analyzed. RESULTS: 434 patients were included in the study, of which 372 were men (85.7%), and the mean age was 55 ± 11.8 years. ALD was present in 162 (37.3%) patients and we found a higher percentage of cases with tremor, flapping and hallucinations in the ALD group. A total of 22 patients (5.0%) died during admission (7.4% with ALD vs 3.7% without ALD; P = 0.087). Among the ALD patients, a relationship between mortality and the presence of anemia (Odds ratio [OR]=4.6 Confidence interval [CI]95% 1.1-18.8; P = 0.034), low level of consciousness (OR=4.9 CI95% 1.1-21.2; P = 0.031) and previous diagnosis of cancer (OR=10.3 CI95% 1.8-59.5; P = 0.009) was detected. Complete recovery was achieved by 27 patients with ALD (17.8%) and 71 (27.8%) without ALD (P = 0.030). CONCLUSION: the association of WE and ALD in patients with AUDs is frequent and potentially linked to differences in clinical presentation and to poorer prognosis, as compared to alcoholic patients with WE without ALD.

Comparative Study on the Quality of Microcrystalline and Epitaxial Silicon Films Produced by PECVD Using Identical SiF4 Based Process Conditions.

Hydrogenated microcrystalline silicon (µc-Si:H) and epitaxial silicon (epi-Si) films have been produced from SiF4, H2 and Ar mixtures by plasma enhanced chemical vapor deposition (PECVD) at 200 °C. Here, both films were produced using identical deposition conditions, to determine if the conditions for producing µc-Si with the largest crystalline fraction (XC), will also result in epi-Si films that encompass the best quality and largest crystalline silicon (c-Si) fraction. Both characteristics are of importance for the development of thin film transistors (TFTs), thin film solar cells and novel 3D devices since epi-Si films can be grown or etched in a selective manner. Therefore, we have distinguished that the H2/SiF4 ratio affects the XC of µc-Si, the c-Si fraction in epi-Si films, and the structure of the epi-Si/c-Si interface. Raman and UV-Vis ellipsometry were used to evaluate the crystalline volume fraction (Xc) and composition of the deposited layers, while the structure of the films were inspected by high resolution transmission electron microscopy (HRTEM). Notably, the conditions for producing µc-Si with the largest XC are different in comparison to the fabrication conditions of epi-Si films with the best quality and largest c-Si fraction.

Patient-Centered Treatment Outcomes with Full-Arch PEEK Rehabilitation Supported on Four Immediate or Conventionally Loaded Implants. A Randomized Clinical Trial.

This study aims to assess the treatment outcomes (functional and subjective) of full-arch fixed hybrid rehabilitations made of PEEK (poly-ether-ether-ketone) with milled crowns of nano-filled composite (NFC) supported on four to six implants. In this randomized clinical trial, 34 edentate patients in the upper and/or the lower jaws were treated with the fixed hybrid dentures. In 16 patients (47.1% of the sample), the implants were loaded immediately (IL) by means of a provisional fixed rehabilitation made of PMMA (polymethylmethacrylate) screwed on Multi-Unit (MU) abutments connected after emplacement of the implant; however, in the counterparts (n = 18) these MU abutments were covered by healing caps and were left unloaded during two months (conventional loading protocol-CL), when all patients received a fixed hybrid PEEK-NFC rehabilitation on the upper and/or the lower jaw. Treatment outcomes were assessed 12 months after prostheses delivery. Functional outcomes were calculated according to masticatory performance, estimated by mixing ability tests of two colored chewing gums after ten chewing strokes, by the occlusal force/area recorded by pressure-sensitive sheets, and by electromyography of masseters and temporal muscles at maximum biteforce. The subjective outcomes of the treatment were assessed using both the oral satisfaction scale (visual analog scale) and the Spanish version of the Oral Health Impact Profile (OHIP-20). The findings of the present study showed that treatment with fixed PEEK-NFC hybrid prostheses significantly improved the masticatory performance, bite force, occlusal pattern, quality of life, and satisfaction, with the IL group being those with significantly higher occlusal bite forces and greater satisfaction in comparison with CL group. It should be concluded that PEEK-NFC hybrid prostheses can improve several patient-centered outcomes and that loading protocol significantly affects the patient's self-rated satisfaction.

Comparative Effectiveness of Parent-Based Interventions to Support Injured Children.

OBJECTIVES: A comparative effectiveness trial tested 2 parent-based interventions in improving the psychosocial recovery of hospitalized injured children: (1) Link for Injured Kids (Link), a program of psychological first aid in which parents are taught motivational interviewing and stress-screening skills, and (2) Trauma Education, based on an informational booklet about trauma and its impacts and resources. METHODS: A randomized controlled trial was conducted in 4 children's hospitals in the Midwestern United States. Children aged 10 to 17 years admitted for an unintentional injury and a parent were recruited and randomly assigned to Link or Trauma Education. Parents and children completed questionnaires at baseline, 6 weeks, 3 months, and 6 months posthospitalization. Using an intent-to-treat analysis, changes in child-reported posttraumatic stress symptoms, depression, quality of life, and child behaviors were compared between intervention groups. RESULTS: Of 795 injured children, 314 children and their parents were enrolled into the study (40%). Link and Trauma Education was associated with improved symptoms of posttraumatic stress, depression, and pediatric quality of life at similar rates over time. However, unlike those in Trauma Education, children in the Link group had notable improvement of child emotional behaviors and mild improvement of conduct and peer behaviors. Compared with Trauma Education, Link was also associated with improved peer behaviors in rural children. CONCLUSION: Although children in both programs had reduced posttrauma symptoms over time, Link children, whose parents were trained in communication and referral skills, exhibited a greater reduction in problem behaviors.