Biology Open

Background: Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory airway disease associated with impaired mucociliary clearance and persistent inflammation. While prior work has focused on inflammatory and molecular pathways, the physicochemical properties of mucus itself remain poorly characterized. This study aimed to define compositional and biophysical features of CRS mucus that may contribute to dysfunction. Methods: A prospective cross-sectional study was conducted in 15 adults undergoing endoscopic sinus surgery (11 CRS, 4 controls). Mucus was collected from the middle meatus. Hydration was measured by lyophilization. Ionic composition was quantified using mass spectrometry. Viscoelasticity was assessed via oscillatory shear rheology. Total protein, total carbohydrate, sialic acid (Sia) and fucose (Fuc) content were quantified using enzymatic and chemical assays. Statistical comparisons were performed using nonparametric tests. Results: CRS mucus exhibited significantly higher Ca2+; and Mg2+; concentrations (approximately two-fold; p<0.05) and increased variability in hydration and ion content compared to controls. Rheology showed greater heterogeneity and a non-significant trend toward increased viscoelasticity in CRS. Total protein and carbohydrate content were not significantly different; however, the carbohydrate-to-protein ratio was significantly reduced in CRS (p=0.04). Sia content and Sia-to-carbohydrate ratio were significantly elevated in CRS (p=0.04 and p=0.002), particularly in CRS with nasal polyps. Fuc content did not differ between groups. Conclusions: CRS mucus demonstrates coordinated alterations in ionic composition and glycosylation, characterized by increased cation content, hypersialylation, and reduced carbohydrate-to-protein ratios. These changes may contribute to altered mucus properties and impaired mucociliary clearance, highlighting mucus composition as a potential therapeutic target in CRS.

Background: Quadriceps strength and landing mechanics are two modifiable factors associated with anterior cruciate ligament (ACL) injury risk. Collecting detailed biomechanical data is an arduous task. Identifying a relationship using more easily measured variables, such as quadriceps strength, would offer value for athlete counseling and injury prevention programs. Although quadriceps weakness has been associated with altered landing strategies in ACL-reconstructed (ACLR) individuals, this relationship is less clear in healthy athletes. Purpose: To investigate the association between isokinetic quadriceps strength and peak knee flexion angle during a vertical drop jump in healthy adolescent athletes. Study Design: Secondary analysis of previously collected data. Methods: Healthy adolescent athletes had their dominant leg quadriceps strength measured using an isokinetic dynamometer at 60{degrees}/s from 0-90{degrees} of knee flexion. Landing mechanics were assessed during a vertical drop jump using three-dimensional motion capture synchronized with force plates. Pearson correlation was used to evaluate the association between quadriceps strength and peak knee flexion angle during landing, with statistical significance defined as p < .05. Results: There was a weak negative correlation between quadriceps strength and peak knee flexion angle (p = .017, R = -.22 [-.04, -.38]), suggesting that stronger athletes achieved greater knee flexion angles. Discussion: Greater quadriceps strength was associated with increased peak knee flexion angles during landing; however, the weak correlation suggests that strength explains only a small portion of the variability in landing mechanics. These findings deviate slightly from prior literature in healthy populations but are consistent with studies demonstrating that greater quadriceps strength is associated with achieving greater peak knee flexion in ACLR patients. Accordingly, quadriceps strengthening should remain a key component of multifactorial ACL injury prevention programs.

Introduction Obstructive lung diseases (OLDs) are responsible for high rates of illness and death worldwide. Inflammation, chronic airflow limitation, and bronchial remodeling occur in OLD and eventually result in the unique respiratory sounds. Despite its subjective and having low reproducibility, still traditional auscultation using a manual stethoscope is the main method used to identify the lung sounds. Nevertheless, the combination of recent advancements in digital stethoscopes and AI (Artificial Intelligence) has permitted the objective measurement of lung sounds. Nevertheless, there is a lack of standardized, region-specific databases for AI training and validation. Even though lung sound classification is an emerging aspect in research and telerehabilitation the lobar wise acoustic pattern is still novel due to lack of prevailing database to train AI models. Identifying this gap this study aims to develop an acoustic repository and analyze the data using segmental lung sounds from patients with OLDs and healthy controls through an electronic stethoscope. Methods and analysis This is a cross sectional observational study involving 120 participants (60 OLD patients and 60 healthy controls). Lobar wise acoustic signals will be captured using an electronic stethoscope in healthy and diseases population. The data will be analyzed using Audacity software for annotations and then it will be used for feature extraction and statistical analysis. The acoustic features extracted through Audacity, will include frequency, intensity, pitch, and root mean square (RMS) energy. Repeated measures ANOVA will be applied to compare mean sound intensities across lung segments while Pearson correlation will be used to assess associations with body composition parameters. The data will then be standardized for AI-based diagnostic applications. Ethics and dissemination The study is being reviewed from the Ethics Review Committee, Faculty of Medicine, University of Peradeniya (2025/EC/87) will be sought. Informed consent will be obtained in writing. The dissemination of results will take place through peer-reviewed publications and the creation of a public database containing lung sounds from the region.

Abstract Background: Mean platelet volume (MPV) is a simple, low-cost biomarker that reflects platelet activation. Its prognostic value in septic shock remains controversial. We aimed to determine whether MPV at intensive care unit (ICU) admission is associated with hospital mortality in patients with septic shock. Methods: Retrospective cohort study of consecutive adults with septic shock (Sepsis-3 criteria) admitted to a single ICU. MPV, severity scores (SOFA, APACHE II, SAPS II), procalcitonin, and clinical data were collected. The primary outcome was in-hospital mortality. Spearman correlation, univariate and multivariate logistic regression (with Firth's correction), ROC curves, and subgroup analyses were performed. Results: Fifty-eight patients were included; mortality was 58.6%. MPV did not differ between non-survivors and survivors (13.09 {+/-} 1.37 vs. 12.66 {+/-} 1.45 fL, p = 0.259). MPV showed a weak correlation with procalcitonin ({rho} = 0.394, p = 0.002) but not with severity scores. In multivariate analysis adjusting for age, sex, SOFA and comorbidity count, MPV was not an independent predictor of mortality (OR 1.075, 95% CI 0.682-1.755, p = 0.749). The area under the ROC curve for MPV was 0.598 (95% CI 0.444-0.752), significantly lower than that of SOFA (0.837) and procalcitonin (0.836). Subgroup analyses showed no significant association between MPV and mortality in any stratum. Conclusions: In this cohort of septic shock patients, MPV at ICU admission was not associated with hospital mortality and had poor discriminative ability. Widely used severity scores and procalcitonin remain superior prognostic markers. MPV should not be used as a prognostic tool in septic shock. Keywords: Septic shock, Mean platelet volume, Mortality, SOFA, Procalcitonin, Biomarker

Background Gait impairment is a central sign of cervical spondylotic myelopathy (CSM) that is typically evaluated through subjective patient-reported questionnaires or objective in-clinic measures. These systems require substantial resources to administer and are poorly suited for longitudinal monitoring, however, emerging smartphone applications present an efficient alternative. We developed and assessed the validity of a data processing framework based on the SynapTrack smartphone application to assess gait function in individuals with CSM. Methods Participants completed walking tasks which were recorded on both the SynapTrack app and a gold standard gait mat. Acceleration data extracted from the smartphone by the app were filtered and processed to produce gait cycle features including velocity, step time, waveform features and frequency domain features. Standard gait features were compared across the two methods by correlation and Bland-Altman plots to assess validity. App-based gait features were then compared to the standard modified Japanese Orthopedic Assessment (mJOA) assessment to determine construct validity through correlation and ability to discriminate between individuals with CSM and healthy controls. Finally, intraclass correlation coefficients and coefficients of variation were used to measure test-retest reliability and standard variation across app features. Results A total of 110 participants were included in this study, of which 55 (50%) had CSM, 24 (22%) had peripheral neuropathy, and 31 (28%) were healthy controls. SynapTrack gait measures including velocity, step time, and double support showed strong validity as indicated through Bland-Altman plots and high correlation (>0.8) with mat features. In addition to the gait features, acceleration root mean square, acceleration crest, spectral entropy, and dominant frequency showed strong construct validity compared to the mJOA across correlation (0.2-0.54), trend test (p < 0.001), and AUROC (0.62-0.79) analyses. ICCs showed moderate test-retest reliability (0.52-0.67). Discussion The proposed framework for processing gait data showed strong validity compared to the gold standard mat and high construct validity compared to the mJOA suggesting the utility of the SynapTrack app as an efficient alternative to existing methods. The confirmation of gait metrics related to CSM severity and identification of relevant waveform and frequency domain features present opportunities to use smartphone apps to develop ecologically valid data driven markers of CSM severity.

Background: Complicated acute appendicitis carries a higher risk of postoperative morbidity relative to uncomplicated cases. It remains unclear whether surgical timing (night vs. day; weekend vs. weekday) or surgeon seniority influence short-term outcomes in this high-risk population. Methods: This was a retrospective analysis of the RIFT Turkey dataset restricted to histologically confirmed cases of complicated appendicitis who had undergone laparoscopic appendectomy. Primary exposures were surgical timing (day [n=92] vs. night [n=123]; weekday [n=172] vs. weekend [n=43]) and surgeon seniority (trainee [n=89] vs. consultant [n=126]). The primary outcome was unplanned readmission and/or reintervention within 60 days. Secondary outcomes were conversion to open surgery and length of stay (LOS) >3 days. Propensity score matching (PSM) using RIPASA score (caliper 0.05, SMD <0.1) was performed as a pre-specified sensitivity analysis for each comparison. Results: Night-time surgery was associated with higher frequencies of unplanned readmission / reintervention (12.2% vs. 6.5%; OR 1.99 [95% CI 0.74-5.35], p=0.166) and surgical conversion (9.8% vs. 3.3%; OR 3.21 [0.88-11.72], p=0.064) compared with daytime surgery, neither reaching significance. Trainee surgeons had significantly higher readmission/reintervention rates than consultants (15.7% vs. 5.6%; OR 0.32 [0.12-0.82], p=0.013). PSM-adjusted results also showed similar relationships: night vs. day (readmission OR 2.45 [0.85-7.03], p=0.09; conversion OR 2.84 [0.73-11.1], p=0.13), weekend vs. weekday (readmission OR 1.53 [0.24-9.72], p=0.65), and trainee vs. consultant (readmission OR 0.25 [0.08-0.79], p=0.013). Conclusion: Surgical timing was not significantly associated with short-term outcomes in complicated appendicitis, though night-time surgery showed a consistent trend towards higher complication rates. Surgeon seniority was the only factor independently and significantly associated with unplanned readmission and reintervention in both primary and PSM analyses, indicating the need for senior supervision during out-of-hours procedures. Keywords: complicated appendicitis; surgical timing; night surgery; weekend effect; surgeon seniority; propensity score matching; RIFT Turkey

Objective: To address the need for improved measurement of the ways contraception impacts the baseline menstrual cycle (i.e., contraceptive-induced menstrual changes; CIMCs) by assembling an interdisciplinary, global research collective to rigorously develop a person-centered measure for CIMCs in multiple languages. As the first step, this paper reports on our conceptual model development, which is the foundation for ongoing measure development. Study design: We conducted 18 focus groups with 106 people experiencing CIMCs while using hormonal or intrauterine contraception in Durban, South Africa, Santo Domingo, Dominican Republic, and Portland Oregon, United States. We used a virtual affinity mapping approach to analyze qualitative data, which was the basis of our conceptual model along with relevant theory and related models in the literature. Results: The conceptual model of experiences with CIMCs depicts the baseline menstrual cycle, including CIMCs and conceptually-linked effects and the impacts and perceptions of those CIMCs. We found key domains of changes in pain, bleeding volume, bleeding patterns, and characteristics of blood. Conclusion: Our CIMC conceptual model will inform development of a measure with evidence of validation across three language and global contexts. Adoption of a person-centered, standardized CIMC measurement across trials will improve knowledge and decision-making between methods.

Objective: To identify Dickkopf-1 (DKK1) as a prognostically relevant candidate in head and neck squamous cell carcinoma and to evaluate whether DKK1 and cytoskeleton-associated protein 4 (CKAP4) expression is associated with cervical lymph node metastasis in tongue squamous cell carcinoma (TSCC). Methods: DKK1 was screened using the Human Protein Atlas Pathology Atlas. Immunohistochemical expression of DKK1 and CKAP4 was examined in 54 patients with primary TSCC (cT1-4N0) treated surgically between 2015 and 2020. Nine cases were excluded because of insufficient tissue blocks or inadequate staining quality, leaving 45 evaluable cases. Associations with delayed cervical lymph node metastasis were assessed together with conventional clinicopathological factors, including infiltrative growth pattern (INF) and pathological depth of invasion (pDOI). Results: In public database analysis, high DKK1 expression was associated with poorer overall survival in head and neck squamous cell carcinoma. In the TSCC cohort, pDOI [&ge;]5 mm and INF pattern c were significantly associated with cervical lymph node metastasis. Positive DKK1 and CKAP4 expression were also significantly associated with cervical lymph node metastasis. Furthermore, combined DKK1/CKAP4 positivity, when incorporated with INF and pDOI, provided additional risk stratification, and cases with all 3 factors showed a markedly increased likelihood of cervical lymph node metastasis. Conclusions: Expression of DKK1 and CKAP4 was associated with cervical lymph node metastasis in TSCC. Combined assessment of DKK1/CKAP4 expression with INF and pDOI may improve pathological risk stratification and may help identify patients who require closer neck evaluation and postoperative management.

Abstract Background Cancer is an increasing public health challenge in Kenya, particularly in rural and underserved regions where surveillance systems and diagnostic capacity remain limited. Kilifi County, located along the Kenyan coast, lacks a population-based cancer registry, and data on the local cancer burden is not available. This study aimed to characterize the demographic distribution of patients, cancer burden in the county, and management of cancer cases diagnosed at Kilifi County Referral Hospital (KCRH) over ten years. Methods This retrospective study analyzed the patterns of cancer in Kilifi County using patient records from KCRH during the study period (January 1, 2014, to January 1, 2024). Results A total of 101 patients with cancer were identified, 58% female, with a mean age of 54 years. Most patients were from Kilifi North (47%), with a high proportion reporting no formal occupation (41%) or farming (26%). Esophageal and cervical cancers were the most common (18% each), followed by breast and prostate cancers (5% each), with other malignancies occurring infrequently. Histopathology was the primary diagnostic modality (88%). Staging data were incomplete in 70% of cases; among documented cases, the majority presented with advanced disease (21% stage IV). Due to limited local treatment capacity, approximately half of the patients were referred to tertiary centers for chemotherapy, radiotherapy, or surgery. At data cut-off, 43% had died, 25% were on treatment, and 29% were lost to follow-up, with only 2% completing treatment or under follow-up. Conclusions This study demonstrates a substantial cancer burden in Kilifi County and highlights critical gaps in diagnostic capacity, staging, and continuity of care. Strengthening cancer surveillance systems, expanding diagnostic and treatment infrastructure, and establishing a population-based cancer registry are essential to improving cancer outcomes and advancing equitable care in rural Kenya

To evaluate the international interoperability of food composition databases, we assessed the compatibility of seven national food composition tables with USDA FoodData Central (FDC) using the LLM-based matching method reported previously (Nakagawa and Yamamoto, 2026). Databases from four English-speaking countries (Canada, United Kingdom, Australia, and New Zealand), South Korea, and Japan were compared with 8,158 USDA FDC entries (SR Legacy and Foundation Foods, excluding Survey/FNDDS). Match rates varied by country (62.0-89.7%) and food category. After excluding six USDA categories unsuitable for cross-national comparison, 45.2% of the remaining 6,290 entries were not matched by any country. Canada showed the highest concordance, reflecting shared North American food supply. Japan and South Korea showed similar low coverage for vegetables and spices. These findings suggest that while USDA FDC represents a practical foundation for a globally comprehensive food composition database given its breadth, systematic incorporation of country-specific foods and classification schemes will be necessary to achieve true international interoperability.

Objectives: The aim of mammographic screening is the early detection of invasive cancers. In the era of artificial intelligence (AI), this tool may improve diagnosis of earlier stages. The purpose of this study was to assess the impact on selected quality indicators retrospectively. Method: The data source was the Breast Cancer Screening Registry using data from one Screening Unit that currently uses AI routinely. The indicators of the cancer detection rate (CDR), further assessment rate (FAR), and recall rate (RR) in the year 2023, when AI was used, and the year 2022, without AI, in women aged 45-69 were compared. The statistical evaluation used the chi-square test and logistic regression adjusting for the effects of age, a woman's risk level, and the screening round at a 5% significance level. Results: In 2022, without AI, 4,034 women aged 45-69 were included, compared with 4,049 women in 2023 when AI was used. This study showed a non-significant increase in CDR from 5.0 breast cancers detected per 1,000 women (non-AI assessment) to 5.2 (AI-assisted assessment), p = 0.919; OR (95% CI): 1.034 (0.542-1.974), a significant decrease in the FAR from 5.2% to 3.9%, p < 0.001; OR (95% CI): 0.665 (0.529-0.836), and a decrease in RR from 2.4% to 1.9%, p = 0.083; OR (95% CI): 0.754 (0.548-1.037). Conclusion: AI has the potential to be a useful tool in the early detection of breast cancer by improving quality through a decrease in FAR and RR, while probably maintaining CDR.

Sex work is diverse and includes a broad range of people and settings. Over the last thirty years, a large proportion of public health emergencies of international concern (PHEIC) have involved infections transmitted through sexual or close contact and in sexual networks (WHO 2024). Sex workers can face increased disadvantage in relation to these public health emergencies. Given the significant health inequalities sex workers can face, they should be eligible to receive targeted and tailored health support to reduce health protection risks (Hester 2019; Jeal and Salisbury 2004a). However, they are often not explicitly eligible for targeted and tailored support due to a lack of information on incidence, prevalence of disease, and even more basic data such as reliable estimates of the number of sex workers in the UK. Accordingly, the aim of this paper is to determine a population size estimate, with uncertainty, that is more robust than those currently available. In this study, we apply Bayesian Evidence Synthesis to bring together historic estimation efforts with recent ONS National Population Estimates and Genito-Urinary Medicine Clinics Attendance Data (GUMCAD) from the UK Health Security Agency (UKHSA). A key feature of our model is the embedding of uncertainty from each input study in model priors, hence propagating it through to our final estimate. The Bayesian evidence synthesis model estimated a total of 84,000 sex workers in the United Kingdom (95% credible interval: 49,000-130,000), representing 0.121% of the current UK population.

Introduction: Menstrual cycle phase has been proposed as a source of intra-individual variability in resting energy expenditure and the thermic effect of food in premenopausal females, yet studies examining the thermic effect of food across menstrual cycle phases report conflicting findings. Methods: This protocol describes a secondary analysis of prespecified outcomes from a non-randomized, two-period crossover trial primarily designed to assess postprandial plasma triglyceride concentrations across menstrual cycle phases (ClinicalTrials.gov: NCT07459465) in 12 premenopausal females aged 18-30 years, free of chronic disease and hormonal contraceptive use, recruited in Ottawa, Canada. Participants complete two experimental sessions: one in the early follicular phase and one in the mid-luteal phase, each involving consumption of a high-fat meal. Eleven secondary outcomes will be reported: fasting resting energy expenditure, thermic effect of food, respiratory exchange ratio, carbohydrate oxidation rate, lipid oxidation rate, desire to eat, hunger, fullness, prospective food consumption, serum beta-estradiol, and serum progesterone. Masked outcome analyses are performed using linear mixed-effects models. Results: Recruitment began on 26 March 2026; results will be reported in the Stage 2 manuscript. Discussion: Findings from this trial may help clarify whether menstrual cycle phase constitutes a meaningful source of intra-individual variability in energy metabolism, with implications for the design of metabolic research in premenopausal females.

Artificial intelligence (AI) tools have been rapidly adopted by medical researchers, yet whether early career researchers in low and middle income countries possess the awareness and habits needed to use these tools safely remains poorly documented. This study characterized AI adoption patterns, hallucination awareness, and verification and disclosure practices among early career medical researchers in Pakistan. A cross sectional anonymous online survey was conducted among medical students, house officers, residents, physicians, and faculty involved in research or academic work across Pakistan (May 2026). Descriptive statistics and chi square tests were applied to 373 eligible responses. AI use was near universal (99.7%), with 60.3% using AI tools daily. The most commonly reported tool in this sample was Claude (40.5%), followed by ChatGPT (29.2%) and Perplexity (26.0%), though this ranking likely reflects sampling characteristics. Despite high adoption, 59.2% typically did not verify AI outputs before use, and 40.2% had never heard that AI can generate fabricated scientific references. In behavioral vignettes, 36.5% assumed convincing AI generated references were authentic, and 54.2% would continue using remaining AI content after discovering one fabricated reference. Formal research training was strongly associated with consistent disclosure (51.7% vs. 17.1%; chi square=48.43, p less than 0.001). Role, daily use frequency, and research training were not significantly associated with verification behavior. Early career medical researchers in Pakistan demonstrate high AI adoption alongside incomplete hallucination awareness and infrequent verification, a pattern that may carry implications for research integrity. Formal training was the only factor significantly associated with consistent disclosure. Integration of AI literacy into medical curricula and institutional governance frameworks merits consideration.

Background: Generative AI tools can support data-intensive research by writing code, drafting prose, searching analytical possibilities, and stress-testing claims. They can also produce false citations, drift between statistical specifications, and lose continuity across long investigations. This paper describes a practical workflow for using AI systems in empirical research while keeping discovery, verification, and accountability inspectable. Methods: We developed and applied a three-phase human-AI workflow to a case study of 14 elite Ethiopian distance runners. The dataset contained 22,605 GPS-segments collected across 97 consecutive days in late 2025, supplemented by venue and athlete metadata collected in the field. Phase 1 used an autonomous data-exploration tool to pre-filter the hypothesis space across five seeded research questions. Phase 2 used an AI system under direct human guidance to construct candidate findings into numerical claims, verification scripts, and draft text. Phase 3 used an independent AI system in an adversarial role to stress-test methods, statistics, prose, figures, and citations. The workflow was informed by Pearl's distinction between association, intervention, and counterfactual reasoning, with human judgement retained for research direction, interpretation, and final claims. Results: The workflow produced three empirical analyses and a documented correction process. The analyses estimated an altitude-to-sea-level pace correction of +0.10 min/km per 1,000 m at matched heart rate, showed why pooled altitude-surface regression was not identifiable within this venue system, documented method-dependence in heart-rate-based intensity classification, characterised within-venue route variation as a 64/36 path-fixed-to-trail-variable split with the Sululta label resolving into two functionally distinct sub-venues, and reframed the cohort's training through a 3x3x3 prescription lattice grounded in Ethiopian coaching practice. The adversarial phase identified several hallucinated citations, a terminology error between HC1 and cluster-robust standard errors, and several inconsistencies between prose, figures, and computed results. Verification scripts re-derived nearly all numerical claims from the cleaned lap-level data. Conclusions: The case study shows how researchers can organise AI-assisted empirical work so that candidate discovery, claim construction, independent stress-testing, and final accountability remain separated. The workflow did not remove the need for domain expertise or human judgement. Its value was in making the route from candidate finding to manuscript claim explicit, reproducible, and open to challenge. Trial registration: Not applicable.

Introduction Air pollution is a leading driver of cardiovascular disease with a growing body of literature implicating this in worse glucose homeostasis. Increases in fine particulate matter air pollution (PM2.5) are associated with increased blood glucose and hemoglobin A1c across the glycemic spectrum from normoglycemia to prediabetes to all forms of diabetes. Despite strong evidence for positive associations of PM2.5 with dysglycemia, it remains unknown if reducing air pollution exposure through air filtration can effect improvements in glucose. This study aims to test the hypothesis that short-term, in-home air pollution reduction using high efficiency particulate air (HEPA) filtration will improve blood sugar in adults with prediabetes. Methods and analysis This trial is a randomized, double-blind, sham-controlled trial of the effects of lowering air pollution exposure using HEPA filtration on cardiometabolic health in adults with prediabetes living in the New York City area. Participants will be randomly assigned to use bedroom air cleaners, or sham air cleaners, while measuring PM2.5 continuously for 1 month. The primary outcomes will be continuous glucose monitoring metrics measured before and after HEPA air filtration. Exploratory outcomes will include insulin resistance measures, serum biomarkers and transcriptomics measured before and after HEPA intervention. We will quantify effects of HEPA filtration with models using treatment arm (true versus sham filtration) as the independent variable. Secondary analyses will model continuous measures of PM2.5 as the independent variable. Ethics and Dissemination This study has undergone peer review; and the work was supported by Grant 2023-0214 from the Doris Duke Foundation, who had no other role in study design or implementation. The study was registered in ClinicalTrials.gov (NCT05994937) prior to recruitment. Clinical Trials Clinical Trials NCT05994937; https://clinicaltrials.gov/study/NCT05994937

Purpose: To identify, through iterative user-centered design, the auditory biofeedback requirements and sound preferences supporting gait training in children with cerebral palsy (CP), and to determine which feedback variables, sound mappings, and sound types yield clinically viable and movement-interpretable paradigms. Methods: The iterative process spanned two prototype phases. Prototype A comprised seven paradigms demonstrated to two experienced physiotherapists (Workshop 1A). Two of these were subsequently discarded owing to poor sound-movement interpretability and two were modified. Six paradigms were added to Prototype B, demonstrated to four children, five parents, and one therapist (Workshop 1B) and two therapists (Workshop 2B). Data were analyzed using systematic text condensation. Results: Within-child sound preferences varied with energy level and sensory state on a given day. Sound-movement interpretability tended to suffer for paradigms with greater acoustic complexity (e.g. computer-generated music). Therapists endorsed a repertoire spanning both movement quality and movement quantity targets. Participants independently proposed paradigms rewarding restrained and controlled movement, a feedback category absent from the current prototype. Conclusions: Session-level calibration is preferable to fixed sound profiles, requiring real-time interface support for paradigm adjustment. Acoustic complexity must remain subordinate to movement-sound interpretability. Paradigms targeting movement restraint are a development priority unaddressed in the literature.

Background: Accurate evaluation of fine motor abilities is a key aspect of neurological rehabilitation. However, conventional approaches like goniometry are limited by variations among raters and their difficulty in detecting active movement. On the other hand, computer vision-based software delivers non-invasive and quantitative analysis of hand movements. An innovative computer-vision-based software tool, F.A.I.R. Chance(C), was developed to track and analyze individual finger joint movements on a camera-equipped laptop and give real-time numerical feedback. However, its metrics require validation in a healthy population before the tool can be used for clinical purposes. Objective: To assess the reliability and validity of finger movement assessment by the F.A.I.R. Chance computer vision-based tool in healthy adult participants. Methods: An observational cross-sectional study was done at MGM School of Physiotherapy, comprising 30 healthy participants between 18 and 60 years of age. Finger movements like flexion, extension, abduction, and adduction were measured with a standard handheld goniometer. These same finger movements were then measured with the tool at two time points separated by a 30-minute interval to determine the test-retest reliability. The tool's measurements were compared with the goniometric measurements to determine its concurrent validity. Test retest reliability was checked by the Intra-class Correlation Coefficient ICC (2,1), while concurrent validity was tested through Pearson's correlation coefficients. Results: Metacarpophalangeal and proximal interphalangeal joint motions demonstrated moderate to good test-retest reliability (ICC: 0.716-0.953) for the F.A.I.R. Chance tool. However, distal interphalangeal joint movements had lower consistency. Good reliability (ICC: 0.754-0.908) was seen for movements of abduction and adduction in the fingers. Strong concurrent validity for extension movements of the metacarpophalangeal joints (r=0.760-0.914) and moderate concurrent validity for flexion movements of the metacarpophalangeal joints (r=0.427-0.604) was demonstrated for all fingers for the F.A.I.R. Chance tool. Concurrent validity for adduction and abduction movements demonstrated a low to fair correlation with goniometric measurements (r=0.210-0.440). This is consistent with previous research showing poor agreement between goniometry and adduction-abduction movements of the fingers. Conclusion: The F.A.I.R. Chance tool shows good reliability and acceptable concurrent validity to assess fine motor movements in the healthy adult population. This sets a basis for further clinical study of the tool in the target population with fine motor impairments. Keywords: artificial intelligence; assistive technology; computer vision; fine motor evaluation; hand function;

Objective: Cross-sectional studies indicate associations between self-reported social media use and adolescent wellbeing outcomes. We aimed to evaluate longitudinal associations of objectively measured smartphone and social media use with psychosocial wellbeing. Design: Observational study with one year of follow-up Setting: High schools in Finland from 2022 to 2023 Population: 259 adolescent girls (mean age 16.3 years at baseline) Main outcome measures: screenshots depicting smartphone and social media use, Bergen Social Media Addiction Scale (BSMAS), Generalized Anxiety Disorder-7 questionnaire, Body Appreciation Scale 2 (BAS-2) and visual analogue scales (VAS) of mood, tiredness, and loneliness Results: Across one year of follow-up, anxiety, body appreciation, and mood improved, but possible social media addiction increased from 15% to 17%. Social media addiction at baseline was associated with increased anxiety (r=0.29, p<0.001), lower body appreciation (r=-0.15, p=0.022), and more loneliness (r=0.20, p=0.001) at follow-up. Anxiety at baseline was associated with social media addiction at follow-up (r=0.19, p=0.005). The highest quartile of TikTok users reported more social media addiction (BSMAS 19 [IQR 16-21] vs. 17 [IQR 14-20]; p=0.009) and lower body appreciation (BAS-2 32 [IQR 28-38] vs. 35 [IQR 29-40]; p=0.003) than did others. The highest quartile of Snapchat users reported more social media addiction (BSMAS 19 [IQR 15-21] vs. 17 [IQR 14-20]; p=0.007) and tiredness (VAS 21 [IQR 13-32] vs. 26 [IQR 15-35]; p=0.049) than did others. Conclusions: Consistent with cross-sectional studies, social media addiction was associated with poorer psychosocial outcomes across follow-up. Policies to protect adolescents from social media addiction are urgently needed.

Introduction Air pollution is the largest environmental risk to human health. Children are disproportionately affected by air pollution and their exposure is amplified during physical activity. Observed concentrations of nitrogen dioxide in 1 in 4 London school playground exceeds the European limit, but the health impacts of air pollution exposure in London school playgrounds remain unexplored. Our study aims to assess and compare the acute changes in lung function and airway inflammation of primary school-aged children exercising in school playgrounds. Methods and analysis 330 children aged 8 to 11 years from ten London schools will be recruited to complete 90 minutes of physical activity and 90 minutes of rest in their school playground in a randomised crossover design. Pre-, post-, and 24-hour post-exposure oscillometry measurements will be performed with airway resistance at 5 Hz (R5) the primary physiological outcome. Nasal lavage samples will be collected pre-exposure and 24-hour post-exposure for analysis of inflammatory, oxidative, and vascular biomarkers, with IL-6 as the primary biological outcome. Mixed-effects regression models will examine associations between estimated pollutant exposures, exercise and physiological responses.