Applied Sciences

Objective To develop an intelligent predictive indicator system for determining weaning and extubation timing in mechanically ventilated patients, providing a theoretical foundation for clinical decision support systems and intelligent assessment in critical care nursing. Methods A literature review was conducted to identify and extract factors influencing weaning and extubation timing in mechanically ventilated patients, forming the initial item pool for the indicator system. A Delphi expert consultation questionnaire was designed and administered to 21 experts from relevant fields over two rounds. Item screening and revision were performed using the boundary value method, based on the arithmetic mean of importance scores, coefficient of variation (CV), and qualitative expert feedback. Results The effective response rate was 100% for both rounds. The expert authority coefficient (Cr) was 0.89 in the first round and 0.93 in the second round. Kendall's W was 0.244 ({chi}{superscript 2} = 1038.944, P < 0.001) in the first round and 0.079 ({chi}{superscript 2} = 350.343, P < 0.001) in the second round. Following the first round of evaluation of 203 items--which resulted in 13 additions, 23 modifications, and the separation of weaning/extubation dimensions--the second round comprised 211 evaluation items. The final system encompassed 6 primary indicators and 58 secondary indicators (with tertiary indicators under each), covering the complete clinical trajectory: medical history, ICU admission assessment, pre-weaning/extubation assessment, weaning assessment, extubation assessment, and post-extubation outcomes. Conclusion This indicator system was developed through a rigorous process with high expert authority and good consensus. The indicators are clinically comprehensive, nursing-operable, and suitable for intelligent modeling. This system can serve as a core framework for risk warning, timing judgment, and intelligent prediction system development for weaning and extubation in mechanically ventilated patients.

The availability of hearing aids that can connect wirelessly to smartphone technologies via Bluetooth has grown exponentially in recent years. However, there is limited evidence assessing the benefits of user-adjustability afforded by these devices. This study aimed to assess the benefits of smartphone-connected hearing aids and an accompanying application (or app) in new and existing hearing aid users. In this single-centre, prospective, observational study, 44 adult hearing aid users (14 new and 30 existing) were recruited. Participants were fitted bilaterally with smartphone-connected hearing aids that could be adjusted by the user via an app. Self-reported outcome measures were collected at fitting and after seven-weeks of using the device in everyday life. For both new and existing hearing aid users, significant improvements in social participation, hearing-related fatigue, and hearing aid benefit and satisfaction were found. For existing hearing aid users, all outcomes were significantly better for the smartphone-connected hearing aids plus app in comparison to their existing hearing aids that did not connect to a smartphone, all with moderate-to-large clinical effect sizes (d> .6). User-controllability via the app was identified as the key benefit, and most participants (68%) reported that the app met their needs 'extremely' or 'very well'. These results suggest that, when used in conjunction with an app, smartphone-connected hearing aids can improve hearing outcomes due to greater user-controllability to improve listening. Thus, smartphone-connected hearing aids have the potential to facilitate patient-centred care, empowering the individual to successfully manage their hearing loss.

Physical activity (PA) is measured objectively through daily wearable monitoring and mobility capacity tests, and subjectively via patient reported outcomes (mobility perception). This study investigated longitudinal changes in, and relationships between, different measures of PA among older adults recovering from proximal femoral fracture (PFF). Participants (N=201) were classified into four groups by time since surgery at baseline (T1) and followed over two assessments (T2, T3). They wore an accelerometer for 7 consecutive days. Daily PA was measured using cut-point free metrics including Average Acceleration, Intensity Gradient, and intensity of the most active accumulated X minutes (MX: M1-M90). Mobility capacity and perception of participants were evaluated using clinical tests (e.g., 6-minute Walking Test (6MinWT)) and questionnaires (Late-Life Function and Disability Instrument (LLFDI)). MX metrics, particularly M1-M15, increased significantly across the first three groups with higher sensitivity in group 1 (p<0.001). Distance covered during the 6MinWT increased significantly (p<0.01). Three of the seven LLFDI s domains showed the largest significant changes. Overall, sustained, moderate-strong positive correlations were observed between the clinical tests, LLFDI, and short-duration MX metrics in group 3 and 4 at T1, and across all participants at T2 and T3. Thus, MX metrics (M1-M15) can reveal change for daily PA intensities, especially among PFF groups in early recovery groups at T1 and reached the late stage at follow-ups. Clinicians may focus on specific LLFDI s domains to maximize assessment efficiency. The direct links between mobility capacity, perceived mobility, and short-duration MX metrics indicate the potential of these metrics to monitor patients remotely.

Using routinely recorded ventilator data from 16 preterm infants with respiratory distress syndrome, dynamic compliance trajectories after surfactant administration were smoothed with a moving-median filter and characterized with a four-parameter logistic model; 10 infants showed sigmoidal improvement, and among 11 infants who received a second surfactant dose, 6 showed subsequent improvement, suggesting that this approach can visualize and quantify individual compliance recovery as a complementary tool to conventional clinical indicators.

Studies employing and evaluating hearing-aid self-adjustments frequently label the resulting settings as 'preferences' without questioning that label. There is a lack of inquiries into the rationale behind participants' choices or their approach to self-adjustment. We here employed a think-aloud protocol concurrent with individuals' self-adjustments to capture qualitative perspectives on these processes. Thirty-six adult hearing-aid users were asked to verbalise what they were thinking while adjusting to their preference three stimuli (music, speech or speech in noise) using three control interfaces (1 slider jointly controlling bass/treble, 2 sliders separately controlling bass and treble or 3 sliders controlling bass, mid and treble). Audio-video recordings were transcribed, annotated and analysed using inductive content coding and thematic analysis. Participants' approach and navigation of the self-adjustment process was best defined as a continuum between exploratory and anticipatory archetypes, with both types sometimes occurring at different stages within the same adjustment. The exploratory type initially assesses control functions, then selects the optimal setting from available options, and evaluates sound changes affectively in isolation or comparatively against prior settings. The anticipatory type begins by evaluating the sound, identifying issues, adjusting settings guided by past experiences, and evaluating adjustments against the identified issues. Additionally, the descriptors elicited during adjustments generally agreed with expert-based data. The resulting qualitative framework of self-adjustment helps explain both how hearing-aid users navigate the personalisation process and how self-adjustment can promote greater understanding of the options available and greater ownership in that process.

Study design Retrospective cohort study. Objectives To correlate Hounsfield unit (HU) values, using elliptical regions of interest (ROI), that can be easily defined in routine clinical practice with magnetic resonance imaging (MRI) T2-hyperintense area fraction, as a surrogate for paraspinal muscle fat infiltration and to establish specific HU screening thresholds that may be applied with standard picture archiving and communication system (PACS). Methods We included 136 patients (71 men; 61.0 {+/-} 15.4 years) who underwent preoperative computed tomography (CT) and MRI within an 8-week period. Elliptical ROI HU values were measured at L2/3 and L4/5 for erector spinae, multifidus, and psoas major. MRI T2-hyperintense area fraction (Otsu thresholding) served as the fat infiltration reference. Linear mixed-effects (LME) models were used to assess the HU-T2 association and level-specific receiver operating characteristic (ROC) analyses (lower HU value side; n=136 per muscle-level) to identify thresholds for [&ge;]30% and [&ge;]50% infiltration criteria. Results Intraclass coefficients = 0.709 (HU) and 0.857 (T2 fraction); Goutallier weighted kappa = 0.579. In the overall LME, {beta} was -0.880 HU per 1% T2-fraction increase (95% confidence interval -0.935 to -0.825; marginal R2 =0.502); the association was steeper in multifidus ({beta} = -1.020) than in erector spinae ({beta} = -0.753). Psoas major (R = -0.226) was excluded from ROC analyses. Difference between L2/3 and L4/5 HU cutoffs was ~20 HU. The [&ge;]50% criterion revealed higher discrimination. Conclusions Elliptical ROI-based HU measurements may reliably screen paraspinal muscle fat infiltration in erector spinae and multifidus using standard PACS. Specific thresholds may allow practical preoperative evaluation without additional costs or radiation.

Purpose: Conventional clinical acoustic analysis systems are confined to specialized clinical environments, which limits scalable and remote voice monitoring. This study aimed to validate LIS-N, a web-based prototype of a novel mobile application that uses the open-source Praat-Parselmouth library for acoustic feature extraction, against the Computerized Speech Lab (CSL), the clinical reference standard, to assess its potential for telehealth and longitudinal voice assessment before integration into clinical or research workflows. Method: Twenty adult volunteers without voice complaints or diagnosed dysphonia completed three voice tasks across three consecutive days at a tertiary academic voice center in quiet isolated rooms. All participants completed the full protocol across three consecutive days. Voice tasks included Rainbow Passage reading, sustained vowel /i/, and maximum phonation time /a/. Audio was captured concurrently using CSL and the LIS-N prototype, which uses Praat-Parselmouth for acoustic feature extraction. To isolate the contributions of recording hardware and analysis software, four cross-system conditions were also examined. Agreement was assessed using Pearson correlation and Bland-Altman analyses. Session-to-session trajectory correlations were computed to evaluate temporal consistency across systems. Results: Fundamental frequency and Pitch Mean demonstrated excellent agreement across all tasks and conditions (r > 0.98), with robustness to hardware and software differences. Pitch Minimum and Pitch Maximum showed strong agreement during sustained vowel tasks. Energy-based features showed moderate agreement, with variability driven primarily by microphone differences, and same-microphone conditions yielded substantially stronger energy agreement. Perturbation measures including jitter, shimmer and CPP showed consistently poor cross-platform agreement, consistent with prior literature. Conclusion: This study provides proof of concept for the viability of open-source acoustic frameworks as accessible alternatives to proprietary clinical systems, while also identifying limitations. Both systems should be interpreted separately, as each remains reliable within its own framework. The LIS-N prototype demonstrated strong agreement for frequency-based voice measures and consistent longitudinal monitoring, supporting adoption of open-source acoustic tools in telehealth and remote voice monitoring.

Background: Sepsis is a life-threatening condition triggered by infection and associated with dysregulated immune response of the patient followed often by multiorgan dysfunction or failure. In the clinical evolution of sepsis towards organ dysfunction, early initiation of a suited therapy significantly increases patient outcomes and reduces mortality rates. Since electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Methods: We developed an interoperable, time-sensitive CDSS that able to detect systemic inflammation and the different classifications of sepsis (bacterial/viral, suspected/proven, on admission/PICU acquired) in pediatric patients based on the analysis of routine clinical data. This application is provided as part of this publication as an open demonstrator (web application), and the usability and accuracy of the CDSS is shown by a retrospective creation of sepsis outcome labels for a routine data set of 4,655 pediatric patients. As a reference standard, the patients were manually assessed by blinded clinical experts. Results: In comparison with the reference standard, the CDSS achieved sensitivity of 96.9% (95% CI: 80.9-99.6%) and specificity of 99.1% (95% CI: 95.1-99.8%). In the context of a sepsis outcome labeling for 4,655 patients, the CDSS detected 4,342 episodes of inflammation of which 1,723 were classified as sepsis. Conclusions: We demonstrated that our routine-data based CDSS is able to perform a complex sepsis detection process with high diagnostic accuracy. Such CDSS with the ability to differentiate between SIRS, sepsis on admission, suspected and proven sepsis can prospectively support clinical management, monitoring and quality management.

Objectives This study aimed to assess sleep patterns and life satisfaction among employees of a private company in Dubai, United Arab Emirates, and to examine the relationships among sleep quality, life satisfaction, and selected demographic variables. A quantitative descriptive cross-sectional survey design was adopted. Methods A convenience sample of 110 male employees participated in the study. Data were collected using the Sleep Disorder Assessment Scale (16 items; Cronbachs = 0.89) and the Life Satisfaction Scale (5 items). Statistical analysis was performed using SPSS version 29, including descriptive statistics, chi-square tests, and Pearson correlation analysis. Results Most participants (66.4%) were aged 20-30 years, and 82.7% experienced moderate sleep-related problems. Mobile phone use before bedtime was common, with 60.9% reporting occasional use and 35.5% reporting regular use. Overall, 41.8% reported neutral life satisfaction, while 25.5% and 24.6% were slightly and extremely satisfied, respectively. A significant negative correlation was found between poor sleep patterns and life satisfaction (r = -0.389, p < 0.001). Mobile phone use before bedtime and shift work were significantly associated with sleep patterns (p = 0.048). Conclusion Poor sleep quality, particularly among shift workers and frequent bedtime mobile phone users, is associated with lower life satisfaction. Workplace interventions promoting sleep hygiene may enhance employee well-being.

In vitro fertilization (IVF) laboratories equipped with timelapse incubators generate vast quantities of sequential embryo images, yet the absence of standardized, annotated databases impedes the development of reproducible computational tools for embryo assessment. Here we describe the construction of a standardized time-lapse imaging database comprising 631 normally fertilized zygotes from 218 treatment cycles, integrating timelapse image sequences, patient clinical records, and embryo developmental outcomes. We further present a gradient boosting decision tree (GBDT) ensemble framework that integrates zygote morphokinetic parameters-continuous time-series features extracted via a validated CNN-based segmentation algorithm (US Patent US11210494B2)-with conventional embryo assessment grades (categorical features per the Istanbul consensus). The fusion framework employs equal-weight initialization followed by iterative residual-decreasing training to optimally combine heterogeneous feature types. Ablation analysis demonstrated that the integrated model achieved an AUC of 0.78, significantly outperforming morphokinetics-only (AUC 0.71) and conventional-only (AUC 0.65) models, confirming the complementary value of the two data modalities. The database and fusion framework provide a reproducible foundation for embryo development assessment and are generalizable to other multimodal data integration tasks in reproductive medicine.

Hard-tissue surgery requires precise spatial control, while conventional computer-assisted navigation presents guidance separately from the operative field. Augmented reality can bring navigation information closer to the surgeon, but how best to translate this information into intuitive intraoperative guidance remains underexplored. We therefore developed an augmented reality navigation system (ANS) that translates real-time deviations from the planned trajectory into co-located holographic action cues. Evaluation comprised typodont and ex vivo porcine experiments, a randomized crossover study in 40 dental trainees, and eight clinical cases. In porcine mandibles, ANS showed lower angular deviation than static and dynamic navigation (1.78 vs 6.42 and 4.02 degrees). Among trainees, accuracy was comparable with dynamic navigation, while workload was lower (29.18 vs 33.15), usability higher (73.13 vs 68.88), and visual-focus ratings improved (3.55 vs 2.28). Clinical cases supported feasibility. These findings support co-located action guidance as an alternative interface paradigm for surgical navigation.

This brief communication presents two solutions for calculating absolute measures of error from time-difference-of-arrival (TDOA) positioning in underwater acoustic telemetry arrays. First, a Monte Carlo estimation of TDOA positioning error is derived. Next, a computationally inexpensive, approximate solution to the Monte Carlo method is presented. This approximate solution is achieved by solving the Jacobian of a closed-form TDOA positioning model. The positioning error covariance matrix returned from either method can then be used to report the accuracy of TDOA positions or utilized in state-space positioning models. Finally, calculations of the expected radial error are shown which serves as a simple summary statistic for reporting positioning error in real units.

INTRODUCTION: Osteogenesis imperfecta (OI) is a genetic disorder primarily due to mutations in collagen type I-encoding genes, resulting in fragile bones, frequent fractures, pain, and mobility issues. Disease severity and phenotype vary widely, even with the same mutation, suggesting the importance of other factors within the bone microenvironment that influence disease severity. To study the role of such factors, we analyzed bone samples from OI patients and healthy controls using single-cell RNA sequencing to reveal if RNA expression profiles may uncover mechanisms behind OI phenotype. METHODS: Bone samples from surgeries of OI patients and healthy individuals isolated and RNA single-cell sequencing was performed, followed by quality control and bioinformatics analysis. Two healthy and three OI patients were included: two with type-I OI, characterized by a mutation in COL1A1 (collagen type I), and another with type-VIII OI, associated with LEPRE1 mutations, which disrupt the 3-hydroxylation of type I collagen. RESULTS: Clustering and differential expression analysis showed distinct subpopulations in mesenchymal and immune cells. In all OI samples, mesenchymal stromal cell (MSC) proportions were reduced compared to healthy controls. OI type-I patients showed decreased osteoblast numbers alongside an increase in osteoclast precursor cells. Whereas in OI type-VIII, all bone turnover-related cells (osteoblast, osteoclast precursor, and osteoclast) were elevated. Notably, BMP5 and RUNX1 were downregulated in MSCs from both OI types. DISCUSSION: This study demonstrates that the bone marrow microenvironment in OI is significantly altered beyond the known collagen defects. Single-cell RNA sequencing revealed reduced MSC numbers and downregulated osteogenic gene expression. Furthermore, alterations are patient-specific: OI type-I is characterized by reduced osteoblast counts, whereas OI type-VIII exhibits increased osteoblasts and osteoclasts. These findings highlight the critical role of impaired osteogenic differentiation and an abnormal bone remodeling environment in the pathology of OI.

Background: To examine the association between ultrasound-based muscle activity detection, or sonomyography (SMG) derived metrics, and clinical measures of upper-extremity function in individuals with cervical spinal cord injury (cSCI), and to evaluate SMG-based trajectories derived directly from muscle activity as a muscle-level assessment compared to conventional kinematic approaches. Methods: Eight individuals with cSCI (n = 8; American Spinal Injury Association Impairment Scale grades A to C; injury levels C5 to C6) participated. Participants performed a wrist tenodesis based target achievement task while SMG data were collected. SMG derived metrics were correlated with performance-based upper extremity function assessed using the Jebsen Taylor Hand Function Test (JTHFT) and self-reported function assessed using the Capabilities of Upper Extremity Questionnaire (CUE-Q). Associations were quantified using distance correlation (dCorr). Results: Strong associations between SMG-derived metrics and clinical measures were observed. Movement Arrest Period Ratio (MAPR) showed the strongest association with JTHFT performance (dCorr = 0.75), while Time to Peak Velocity (TTPV) demonstrated a moderate association (dCorr = 0.62). Rate of Change of Acceleration (ROCAcc) showed a strong correlation with CUE-Q scores (dCorr {approx} 0.70), and spectral arc length (SAL) showed moderate correlations (dCorr {approx} 0.66). Conclusions: SMG-derived metrics show meaningful associations with both performance-based and self-reported measures of upper-extremity function in individuals with cSCI. These findings suggest that SMG metrics can serve as objective tools to complement clinical assessments for tracking functional status and recovery. Larger studies are needed to confirm these observations.

Ovarian cancer is recognized as the deadliest gynecological malignancy. Diagnosis at advanced stages and the lack of effective screening program lead to poor survival rates, dropping to 17-39 % in stage III-IV diseases. Ultrasound (US) is the primary imaging modality for ovarian structures evaluation, but it is strongly affected by the operator expertise due to the complexity of adnexal masses and the physiological variability of ovarian morphology throughout a womans lifecycle. The International Ovarian Tumor Analysis (IOTA) group introduced definitions and predictive tools to standardize gynecological US interpretation. However, these tools still rely on subjective interpretation, thus highlighting the need for more objective solutions. Recent studies have explored artificial intelligence (AI) algorithms for gynecological US, mainly focusing on adnexal masses classification. Conversely, a robust solution supporting the identification and description of healthy and tumoral ovarian structures is still lacking. This paper proposes OvAi Focus, a framework including (i) a segmentation module for the identification of healthy ovaries, plus solid and cystic components of adnexal masses; (ii) a morphology module for the extraction of IOTA-based keywords to describe adnexal masses morphology. Segmentation module results were compared to ground truth masks, showing DICE scores from 0.62 for functional ovary to 0.87 for the whole adnexal mass. Morphology module was tested through interobserver agreement analysis, obtaining Fleiss Kappa from 0.16 to 0.57 and Percent Agreement from 47 to 90 %, in line with existing literature. OvAi Focus represents an innovative solution which could help overcoming subjectivity in gynecological US imaging interpretation.

Objective: Circadian rhythms are frequently disrupted in patients in the intensive care unit (ICU), potentially worsening clinical outcomes. Continuous enteral nutrition throughout the day and night is common in the ICU, but eliminates feeding-fasting cycles that serve as important timing cues for the circadian system. The objective of this study was to determine the effect of providing enteral nutrition in a cyclic daytime pattern, compared with continuous administration, on circadian rhythmicity in critically ill patients in the ICU. Design: Single-center randomized controlled trial Setting: Mixed medical-surgical tertiary intensive care unit in the Netherlands Patients: Adult ICU patients ([&ge;]18 yr) receiving enteral nutrition. Intervention: Patients were randomized to receive either continuous, or cyclic daytime enteral feeding (08:00-20:00), initiated from the start of nutritional support. Measurements and Main Results: Sixty-two ICU patients were enrolled, of whom 51 were included in the per-protocol analysis. While the amplitude of the 24-hour rhythm in core body temperature did not differ significantly between the cyclic daytime and continuous feeding groups (0.17 [interquartile range: 0.09-0.24] vs. 0.20 [0.13-0.30], p=0.182), the 24-hour rhythm in heart rate was enhanced in patients receiving cyclic daytime feeding, as reflected by significantly higher amplitudes and more synchronized peak times. No significant differences in 24-hour rhythmicity were observed between groups for the other vital signs or melatonin. Conclusions: Our findings suggest that cyclic daytime feeding may strengthen circadian rhythms in critically ill patients. Further studies are warranted to evaluate its impact on clinical outcomes.

Prolonged low-temperature exposure may extend the preservation window of mammalian cells but can also disrupt cellular homeostasis and ultimately compromise cell viability. This study investigated the time-dependent phenotypic and transcriptomic responses of primary canine dermal fibroblasts to sustained hypothermic stress. Passage-three fibroblasts were continuously maintained at 15 for up to 15 days, with samples collected on Days 0, 3, 6, 9, 12, and 15. Cellular morphology, metabolic activity and viability, and apoptosis were evaluated using bright-field microscopy, Cell Counting Kit-8 assays, and Annexin V-FITC/propidium iodide flow cytometry, respectively. RNA sequencing was performed to characterize dynamic transcriptional changes throughout the exposure period. Early low-temperature exposure was associated with relatively preserved cellular morphology and viability, suggesting a transient adaptive response. With increasing exposure duration, fibroblasts exhibited progressive morphological deterioration, reduced metabolic activity, loss of adhesion, and increased apoptosis. Time-series transcriptomic analysis further revealed temporally coordinated and stage-dependent gene-expression programs associated with metabolic regulation, cellular stress responses, structural homeostasis, and cell survival. Integration of phenotypic and transcriptomic data demonstrated that the response of primary canine dermal fibroblasts to 15 was dynamic rather than linear, progressing from early adaptation to cumulative dysfunction during prolonged exposure. These findings provide a framework for defining the low-temperature tolerance of primary canine dermal fibroblasts and may inform the optimization of protocols for their short- to medium-term preservation and transportation.

Structural musculoskeletal pain, defined as pain associated with musculoskeletal conditions of the spine and peripheral joints, afflicts persons widely, independent of demographic, and continues to contribute substantially to disability on a global scale. Osteopathic manipulative treatment (OMT) is a non-invasive therapy performed by osteopathic physicians, encompassing a wide variety of techniques meant to heal the dysfunctions manifesting structural musculoskeletal pain. However, the efficacy of OMT in relieving pain symptomatology remains subject to debate. This meta-analysis examines the effect OMT serves to manage structural musculoskeletal pain, measured on a Visual Analog Scale. Three randomized control studies (RCTs) were included, with a total of 231 participants, 117 of which received OMT as part of pain management treatment, the other 114 receiving other treatment modalities. Using the random effects model, the mean difference between OMT and non-OMT treated groups was -1.80 (-7.31; 3.78). Although this mean difference favors OMT with regard to greater reduction in pain, the finding is not statistically significant. Heterogeneity was found to be extraordinarily high (I2 = 96%) and statistically significant (p = <0.0001), albeit attributed to one of the papers, deemed an outlier. With its removal, heterogeneity was still moderate (I2 = 54.4%). Given these findings, the efficacy of OMT in reducing structural musculoskeletal pain cannot be proven. A significant limitation of this study was a low sample size, consisting of 3 RCTs, reducing statistical power. In addition, there was high heterogeneity between studies. More high-quality RCTs with larger sample sizes, standardized methods, and an examination of a broader set of structural musculoskeletal conditions are necessitated to better evaluate the contribution of OMT in pain reduction. Key Words: Pain Management, Osteopathic Manipulative Medicine, Osteopathic Manipulative Treatment, Structural Pain, Orthopaedics, Knee Arthritis, Shoulder Pain, Cervical Spondylosis

Objectives: The purpose of this pilot study was to investigate (1) if the Speech Intelligibility Index (SII) could be used to identify ears that meet cochlear implant (CI) candidacy beyond the "60/60" referral guidelines and (2) investigate the relationship between SII from patients' own hearing aids (HAs) and SII from optimally fit clinic-stock HAs such that a patient's own HAs can act as a proxy for an optimally fit HA and thus as a referral metric. Design: Prospective study of 23 participants ([&ge;] 18 years) with bilateral moderate to profound sensorineural hearing loss who were CI candidates in at least one ear. SII-Clinic (SII-C) values were recorded with probe-microphone measures using a Verifit II device (NAL-N2 targets) with speech stimuli presented at 60 dB A during patient CI evaluation in either their own HAs or clinic-stock HAs. SII values from participants' own HAs were recorded as part of a larger research protocol, thus SII-Personal (SII-P), with probe-microphone measures using a Verifit I device (NAL-RP targets) with pink noise stimuli presented at 65 dB A. Participants that did not wear HAs did not have SII-P calculated. Data was collapsed across ears. Correlation analysis between SII and best-aided consonant-nucleus-consonant (CNC) was conducted. Benchmark SII values were extrapolated using a best-fit line and CI candidacy rates reflecting various aided CNC scores that could be applied across CI centers. Nested logistic regression models were used to determine if the SII-C could improve model fit beyond the revised ear-specific 60/60 referral guideline. The relationship between SII-C and SII-P was assessed with Wilcoxon signed-rank test and Spearman correlation. Results: Correlation between SII-C and aided CNC score was r = 0.82 (p < 0.001), and the correlation between SII-P and aided CNC was r = 0.46 (p = 0.014). Extrapolated benchmarks of SII were calculated based on different CNC candidacy cutoffs, and benchmarks were able to capture at least 83% of the ears that met CNC candidacy cutoffs. SII was also found to improve model fit with word recognition score (WRS) and pure tone average (PTA) for 60% CNC candidacy cutoff ({chi}2 = 9.7; p = 0.002) and increased discrimination (AUC = 0.89 to AUC = 0.99). There was no significant difference between SII-C and SII-P using the Wilcoxon signed-rank test (p = 0.559). Conclusions: A larger study is needed that includes more ears that do not qualify for a CI; however, the results of this study demonstrate that (1) SII has the potential to be used in conjunction with the 60/60 referral guideline for CI candidacy evaluations and (2) SII can be used as a marker of audibility across different devices, including patients' own HAs, making SII an accessible metric during HA fittings and fine tunings.

We propose a stochastic epidemic model to describe the spread of avian influenza in a network of poultry farms. We consider three main paths of infection transmission between poultry farms: from nearby farms, mainly through airborne diffusion; from farms belonging to the same company, through shared veterinarians, forage providers and similar; from undetected small farms or wildlife. From the parameter estimation, based on a modified Expectation-Maximization algorithm, we infer that approximately 63% of the farms were infected from nearby ones, with an infection force declining with distance; of these, more than one third belonged to the same company of the estimated infector. About 20% were infected from premises belonging to the same company but farther away than the distance threshold of 2 km; the remaining ones from wildlife or unidentified sources. These estimates have been validated by a comparison with genetic data, available for a subset of the farms: the genetic distance between two farms identified, with high probability, as an infector-infectee pair is much lower than between random pairs. These results may help in the implementation of tailored prevention and control measures for future outbreaks.