Applied Sciences

Background Inflammatory markers play an important role in the pathophysiology of Lumbar disc herniation (LDH). This study presents a comprehensive multi-assessment of the inflammatory landscape by combining serum inflammatory cytokines quantification, their diagnostic performance, associations with radiological features, and integrating the experimental findings into an in-silico protein-protein interaction network. Methods A multifaceted study design was utilized to quantify and compare the distribution of selected inflammatory cytokines in patients with LDH and control subjects. The diagnostic ability of these cytokines was assessed using receiver operating characteristic curve analysis. The cytokines values were correlated with selected radiological findings including disc herniation subtypes (protrusion, extrusion, and sequestration), and further categorized as contained and non-contained in patients using a Spearmans rank correlation test. Additionally, computational analysis was performed to identify the central hubs and functionally enriched pathways. Results In patients with LDH, IL-6 and IL-1{beta} showed statistically significant (IL-6: p < 0.001; IL-1{beta}: p = 0.001) rise, but IL-6 showed high diagnostic and discriminative power (AUC = 0.99; cut-off: 19.99 pg/mL). Further IL-1{beta} exhibited a positive correlation with non-contained disc herniation (extrusion and sequestration), while displaying a significant (p < 0.05) negative correlation with protrusion. In silico analysis identified IL-1{beta}, IL-8, TNF-, IL-6, IL-1, CSF2, CSF3, and IL-10 as central hubs, with IL-1{beta} being the top ranked hub in determining functionally enriched cytokine-cytokine receptor interaction. Conclusions Study confirmed IL-6 as a powerful diagnostic marker for LDH, while IL-1{beta} aids in determining contained and non-contained disc herniation. Further, IL-1{beta} was identified as the central hub, triggering functionally enriched pathways in the pathogenesis of LDH.

ObjectiveOnly preliminary investigations on the use of the 445 nanometer wavelength blue light laser (BLL) for various laryngeal pathologies have been described. Currently, no standard exists for reporting treatment technique and tissue effect with this modality. Here, we aim to establish and validate a classification system to describe laser-induced tissue effects. Study DesignRetrospective video-based study for classification development and reliability validation. MethodsVideo recordings from procedures performed with the BLL by multiple academic laryngologists were retrospectively reviewed. A preliminary 6-point classification (BLL 1-6) was developed based on expert consensus. Thirteen additional procedural clips were independently rated utilizing the classification schema to assess perceived tissue effect, and measure inter- and intra-rate reliability. ResultsThe final 5-point classification system (BLL 1-5) included angiolysis, blanching, tissue vaporization, ablation with mechanical tissue removal, and cutting. The consensus of the combined reviewers in rating all cases was 89% (58 of 65). Complete consensus was not achieved in 11% (7/65) of cases. Of those incorrect, 57% (4/7) were of clips illustrating the BLL-2 classification. Intra-rater reliability amongst the reviewers was 100%. ConclusionTissue effect of the 445 nm blue light laser can reliably be standardized with this proposed classification system. This rating system can be used to facilitate future systematic study of outcomes and effective communication between laryngologists and trainees.

The human auditory system decomposes complex sounds into distinct components via a collection of processing steps. Knowing whether Spiral Ganglion Cells (SGCs) play an active role in the decoding of complex sounds can facilitate the development of Cochlear Implant (CI) coding strategies and clinical assessment tools. Early animal studies reported SGCs being similar across different characteristic frequencies (CFs). In this study, human electrically evoked compound action potentials (eCAPs) were analysed to probe the relationship between the reciprocal of CF and the duration of the eCAP. A significant relationship could indicate that SGCs may not simply be passive cables. eCAP datasets from 6 published studies (175 CI users, 1243 recordings) were analysed and their peaks were automatically labelled. The n1p2 latency was derived for each recording as a proxy of the action potential duration. The CF of each recording was estimated by mapping the average insertion angle of the electrode to the human SGC map. A weak but statistically significant relationship was observed between the n1p2 latency and the reciprocal of CF (random-effects model with random intercepts for subject, r = 0.09, p = 0.024, n= 450) supporting the hypothesis that lower CF is associated with slower repolarisation (longer n1p2 latency) in human spiral ganglion cells.

An increasing number of studies highlight the role of saccadic remodulation in compensatory mechanisms following vestibular injury, and the reappearance of SHIMP saccades correlates with symptom improvement measured by the Dizziness Handicap Inventory (DHI). To investigate the influence of attentional processes and working memory on visuo-vestibular interaction, three independent but interrelated experiments were conducted. In the first two experiments, healthy subjects and patients with unilateral or bilateral vestibular deficits underwent vHIT in SHIMP mode and the Functional Head Impulse Test (fHIT), performed first separately and subsequently simultaneously. Mean latency and clustering of SHIMP saccades, together with Landolt C recognition rates, were analyzed. Differences between separate and combined protocols were assessed, and, in patients, correlated with symptom severity measured by the DHI, to determine whether the near-simultaneous execution of tasks mediated by shared parietal cortical substrates influenced performance. In the third experiment, vHIT in HIMP mode and fHIT were performed using separate and combined protocols to evaluate whether recognition-related cognitive load affected recovery saccade latency and clustering. Results suggest that visual recognition modulates visuo-vestibular interaction, supporting integrated dual-task protocols for ecological balance assessment and helping explain clinical discrepancies.

Introduction Cervical spondylotic myelopathy (CSM) surgery is frequently associated with residual neurological deficits, partly due to unrecognized dynamic spinal cord compression on conventional MRI. Current static imaging may miss position-dependent stenosis, resulting in insufficient or inappropriate decompression. This study aims to evaluate whether dynamic MRI-guided individualized surgery improves neurological outcomes compared to conventional MRI-based planning. Objectives This study aims to examine the association between dynamic MRI-guided surgical planning and neurological recovery in cervical spondylotic myelopathy, and to evaluate its role in identifying responsible segments, avoiding excessive surgery, and improving clinical outcomes. Methods This single-center retrospective cohort study will include 300 patients who underwent cervical spine surgery between January 2020 and December 2025 at the First Affiliated Hospital of Guangxi University of Chinese Medicine. Patients will be categorized into the dynamic MRI-guided group (n=150) or conventional MRI-based group (n=150) based on preoperative imaging modality. 1:1 propensity score matching will be performed using age, sex, BMI, disease duration, baseline mJOA score, and number of compressed segments. The primary outcome is the rate of improvement in the mJOA score at 6 months postoperatively. Secondary outcomes include VAS, NDI, reoperation rate, and time to first complication. Between-group comparisons will use t-tests/Mann-Whitney U tests for continuous variables, {chi}{superscript 2} tests/Fisher's exact tests for categorical variables, and Kaplan-Meier estimates with the log-rank test for time-to-event outcomes. A two-sided P<0.05 will be considered significant. Analyses will be performed using R software (version 4.4.1). Ethical approval was obtained from the Medical Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine (Approval No. 2025-080-KY-01) from February 06, 2026 to February 05, 2027. Expected outcomes We hypothesize that dynamic MRI-guided surgical planning will improve neurological recovery and decompression accuracy in cervical spondylotic myelopathy, providing evidence for optimized preoperative imaging and precision spine surgery.

Purpose: This research sought to develop a low-cognitive-load speech-in-noise test based on consonant confusions with the potential for assessing hearing-aid benefit. Methods: Vowel-consonant-vowel (VCV) stimuli with added speech-shaped noise were presented as a closed-set consonant identification task. Initially, consonant-confusion matrices were used to select, from a larger set of consonants and vowel contexts, a set of ten consonants and associated signal-to-noise ratios (SNR) that were sensitive to hearing loss. The sensitivity of the qVCV test to hearing loss was validated by comparing predicted pure-tone average (PTA) hearing thresholds with their audiometric PTA. Clinical viability of the qVCV test was assessed by comparisons to the QuickSIN test. Hearing-aid benefit was assessed by comparing test scores in unaided and aided conditions. Results: The consonants most sensitive to hearing loss were /b d g t k v z s [esh] n/ in the vowel context /[a]/. A cross-validated prediction of PTA had a mean-absolute error of 5.7 dB. The repeatability of qVCV at 50 trials was equivalent to the QuickSIN average of two lists. Hearing-aid benefit was quantified as a decibel reduction in hearing loss. Conclusions: qVCV and QuickSIN performed similarly when test times are equated. The advantages of qVCV include lower cognitive demand, fewer learning eeects, and automated scoring. PTA predicted by qVCV which greatly exceeds audiometric PTA may indicate either cognitive deficits or cochlear neural degeneration. The qVCV quantification of hearing-aid benefit may have clinical value

Candidiasis pose a serious health threat, stimulating efforts to develop new antifungal agents and alternative therapies. Given the high mortality of fungal infections and the historical use of natural remedies, there is a growing interest in integrating natural substances into modern treatments. It is particularly important to explore interactions between home remedies and clinically approved antifungals to avoid harmful combinations or enhance beneficial effects. In this study, the chemical composition of the ethanolic extract of propolis (EEP) using UHPLC-DAD-QqTOF-MS was analyzed. The interactions of this extract with several antifungal agents against four yeast pathogens causing candidiasis: Candida albicans, Nakaseomyces glabratus, Pichia kudriavzevii, and Candida auris were investigated using Checkerboard Titration Assay, Growth Kinetics, and Disc-diffusion assay. Also, a novel simulated infection model was proposed. The results showed synergistic interactions between EEP and amphotericin B, and additive effects with nystatin. Synergy and additivity with fluconazole and voriconazole were observed, but limited to C. albicans and N. glabratus. In contrast, antagonistic interactions were noted with caspofungin, clotrimazole, and ketoconazole, which may have clinical relevance. Additionally, positive interactions with 2-phenoxyethanol and silver nanoparticles (AgNPs) suggest potential practical applications. Propoliss synergistic properties could expand antifungal strategies and support the development of multi-target, resistance-preventing therapies.

ABSTRACT Background Medical students face demanding academic schedules and elevated stress levels, predisposing them to poor sleep quality. Sleep hygiene, a set of behavioural and environmental practices aimed at optimising sleep, has been identified as a modifiable determinant of sleep quality, yet its role among medical students in Sudan remains unstudied. Objectives To assess current sleep hygiene practices among medical students at UMST and determine their association with sleep quality outcomes. Methods A facility based cross-sectional study was conducted at UMST among 240 medical students from three academic batches (3rd, 4th, and 5th year), selected via stratified random sampling. Data were collected using two validated self administered instruments: the Pittsburgh Sleep Quality Index (PSQI) and the Sleep Hygiene Index (SHI). Descriptive statistics, independent sample t tests, one way ANOVA, chi-square tests, Pearson correlation, and binary logistic regression were performed using SPSS version 23. Results Poor sleep quality (PSQI >5) was prevalent in 72.1% of participants (mean PSQI 7.25 +/- 2.66), and poor sleep hygiene (SHI >16) in 92.5% (mean SHI 27.1 +/- 7.9). SHI score (continuous) was the only significant independent predictor of sleep quality on logistic regression (OR = 1.13 per unit increase; 95% CI: 1.08-1.19; p < 0.001), equivalent to a 3.4-fold increase in odds per 10-unit rise in SHI score. Female sex was additionally identified as a significant predictor (OR = 1.88; 95% CI: 1.00-3.53; p = 0.049). A significant positive correlation was observed between PSQI and SHI scores (r = 0.359, p < 0.001). Conclusion Poor sleep hygiene is highly prevalent among UMST medical students and is the most significant modifiable predictor of poor sleep quality, with each unit increase in SHI score increasing the odds of poor sleep quality by 13%. These findings highlight a gap in sleep health education within Sudanese medical institutions and support the integration of targeted sleep hygiene interventions into the medical curriculum. Keywords: sleep hygiene; sleep quality; medical students; Sudan; PSQI; SHI; cross-sectional study

Parkinson's Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches to capture the complexity of the disease and its clinical heterogeneity. This study proposes an analytical pipeline to evaluate Brain-Heart Interplay (BHI) as a novel systemic biomarker for neurodegeneration and healthy ageing. In this study we assessed BHI across three open-source datasets (EEG and ECG signals). We compared Healthy Young, Healthy Elderly, and PD patients in resting state to investigate the effects of ageing and cognitive performance. Additionally, we studied BHI trends in PD patients in the moment of freezing of gait (FOG). Methodologically, brain network organization was quantified using coherence-based EEG connectivity and graph theory, while heart activity was analyzed through Poincare plot-derived measures of cardiac autonomic activity. The coupling between these two systems was measured using the Maximal Information Coefficient to capture linear and non-linear dependencies between global cortical organization and cardiac autonomic outflow. The results demonstrate that BHI is a sensitive biomarker for detecting early multisystem dysfunction in both neurodegeneration and ageing. Furthermore, the identification of specific BHI trends during FOG onset suggests new opportunities for understanding the physiological mechanisms driving motor complications in PD. Our proposed pipeline provides a guiding tool for large-scale physiological assessment in clinical research.

Adenoid cystic carcinoma (ACC) of salivary gland is a "immune-cold" tumour. Annexin A3 (ANXA3) is an apoptotic protein found to be participating in immune cell infiltration in tumour microenvironment (TME) of various cancer cases. Significant low expressions of ANXA3 protein found in adenoid cystic carcinoma. We hypothesized overexpressing ANXA3 transforms ACC "cold" TME to "hot". We cultured UM-HACC-2A and UFH2 spheroids on extracellular matrix and co cultured them with peripheral blood mononuclear cells. We functionalized FDA (The Food and Drug Administration) approved Poly(lactic-co-glycolic acid) PLGA nanoparticles with anti-cMyb antibody and ANXA3 recombinant protein using streptavidin-biotin conjugation. Upon overexpressing ANXA3 in ACC spheroids in immune coculture model using functionalized nanoparticles, significant increase of tumour infiltrating lymphocytes and decrease in the size of the ACC spheroids observed. Apoptotic profiler assay further confirmed significant upregulation of apoptotic proteins, some of them participate in immune infiltration. Overall, this project exhibits promising results showing potential approach to convert ACC into an immune "hot" tumour.

Hemoglobinopathies are inherited disorders of hemoglobin, most notably sickle cell anemia and thalassemia. These conditions result from mutations in the globin genes, leading either to structural abnormalities in the globin chains or to reduced synthesis of normal globin chains. Hemoglobinopathies is a worldwide health problem according to the World Health Organization; it affects mostly the indigenous Tharu groups in Nepal. Both the global and local rates of illness and death associated with these diseases are on the rise. The objective of this study was to assess the presence of hemoglobinopathies and common mutations of the beta-globin gene within the Tharu population in western Nepal. A cross-sectional study of 1,400 Tharu individuals was conducted among individuals obtained through hospitals within the Banke district, Bardiya district, and Kailali district in western Nepal. A thorough hematological analysis was done with the use of a Sysmex XN-350 analyzer. Hemoglobin variants were detected via high-performance liquid chromatography (HPLC). The molecular characterization of the seven most common mutations of {beta}-thalassemia was performed on a subset of 20 confirmed cases by using a real-time PCR kit.The total number of cases diagnosed with hemoglobinopathies was 14.43% (n=202 out of 1,400). Sickle cell trait (HbAS) was reported as the most prevalent type of Hemoglobinopathies (8.50% of population), followed by {beta}-thalassemia trait (4.00%). In addition to these disorders were sickle cell disease (HbSS), HbE trait, and compound heterozygous states. Hematological parameters differed significantly across types of hemoglobinopathies, and the patterns of microcytic, hypochromic, and hemolytic anemia were also distinct. Commonly documented symptoms included fatigue and joint pain (42.5% and 23.1%, respectively). Molecular characterization of {beta}-thalassemia cases demonstrated that most individuals were compound heterozygotes with IVS1-6 (T>C) as the most prevalent variant. The research identified that the Tharu population in western Nepal has a significant burden of hemoglobinopathies (especially sickle cell trait and {beta}-thalassemia), highlighting the requirement for appropriate screening programs, genetic counseling and public health strategies to help manage and prevent these conditions within this particular region.

This research demonstrates that the trans-aqueduct approach is a feasible, minimally invasive access pathway to the third ventricle, offering a potential route to the deep brain for therapeutic technologies. Further pre-clinical investigation is required to thoroughly evaluate physiological tolerance, trauma risk, and the long-term implications of intraventricular implantation. The third ventricle is a high-value site for neuromodulation due to its proximity to deep-brain targets, including the subthalamic nucleus (STN) and globus pallidus internus (GPi). This study defined the anatomical pathway; and evaluated the technical feasibility of retrograde access to the third ventricle via the cerebral aqueduct using minimally invasive interventional techniques. Evaluation was conducted in three phases using human MRI datasets (n=16; mean age 48.4 years) and cadaveric specimens (n=6; mean age 88.2 years). Phase 1 involved morphometric MRI analysis of the aqueduct and ventricles. Phase 2 tested trans-aqueduct access on cadaver specimens via fluoroscopically guided guidewires and catheters. Phase 3 utilized direct anatomical dissections on cadaver specimens (n=3) to morphometrically measure the third ventricular cavity and its relationship to deep-brain nuclei. Measurements across the sample groups showed a mean aqueduct diameter of 1.6 mm (SD=0.14). Third ventricle dimensions averaged 27.6 mm (ventral-dorsal), 19.9 mm (caudal-cranial), and 5.7 mm (lateral). Successful access to the third ventricle was achieved in 83% (5/6) of cadaveric specimens. The optimal technical configuration utilized a 0.018'' angled-tip guidewire and 5-6 Fr catheters; the aqueduct accommodated diameters up to 2.0 mm with minimal resistance. The STN and GPi were localized within 5-20 mm of the ventricular volumetric centroid. The trans-aqueduct approach is a technically feasible, minimally invasive pathway for accessing the third ventricle. This route offers a potential alternative for the delivery of therapeutic neurotechnologies. Further research is required to assess physiological tolerance, trauma risk, and the long-term safety of intraventricular implantation.

International guidelines for low-frequency electromagnetic field exposure (LF EMF) are primarily intended to prevent substantiated adverse effects. In the frameworks, limits on internal electric fields are linked to external exposure levels through computational dosimetry. However, the relationship between internal electric fields and these adverse effects remains incompletely understood. In particular, current approaches often overlook the morphological complexity and diversity of cortical neurons, which may limit the realism of neuronal activation estimates used to support these assessments. This study evaluates LF EMF-induced neural activation using 25 morphologically realistic neuron models spanning all cortical layers, embedded within 11 detailed human head models. The internal electric fields were simulated for uniform magnetic field exposures (100 Hz-100 kHz) along the three anatomical directions, and excitation thresholds were computed using a multi-scale framework combining voxel-based dosimetry with biophysical neuron simulations. A real-world exposure scenario involving a child near an acousto-magnetic article-surveillance deactivator was also analyzed. Thresholds varied across cell type, morphology, cortical location, subject anatomy, frequency, and exposure direction, with L2/3 pyramidal, L4 basket, and L5 thick-tufted pyramidal cells showing the lowest thresholds. Despite this variability, all simulated thresholds were conservative with respect to the basic restrictions and dosimetric reference limits set by IEEE ICES and ICNIRP. The smallest margin occurred at 100 kHz, where the threshold remained a factor of 2.8 above the corresponding limit. These findings indicate that current LF EMF exposure limits remain conservative when evaluated using highly detailed, morphology-based CNS activation models.

Background: Radiographic detection of caries lesions adjacent to restorations is challenging due to limitations of two-dimensional imaging and difficulties distinguishing true lesions from restorative or anatomical radiolucencies. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) have been introduced to assist radiographic interpretation; however, different AI tools may yield variable diagnostic outputs, and their comparative performance remains unclear. Objective: To compare the diagnostic performance of commercial and experimental AI algorithms for detecting secondary caries lesions on bitewings. Methods: This cross-sectional diagnostic accuracy study included 200 anonymized bitewings comprising 885 restored tooth surfaces. A consensus group reference standard identified all surfaces with a caries lesion and classified each lesion by type (primary/secondary) and depth (enamel-only/dentin-involved). Five commercial (Second Opinion, CranioCatch, Diagnocat, DIO Inteligencia, and Align X-ray Insights) and three experimental (Mask R-CNN-based and Mask DINO-based) systems were tested. Diagnostic performance was expressed through sensitivity, specificity, and overall accuracy (95% CI). Comparisons used generalized estimating equations, adjusted for clustered data. Results: Specificity was high across all systems (0.957-0.986), confirming accurate recognition of non-carious surfaces, whereas sensitivity was moderate (0.327-0.487), reflecting frequent missed detections of enamel and dentin lesions. Accuracy ranged from 0.882 to 0.917, with no significant differences among models (p >= 0.05). Confounding factors, such as radiographic overlapping, marginal restoration defects, and cervical artifacts, were the main sources of misclassification. Conclusions: AI algorithms, regardless of architecture or commercial status, showed similar diagnostic capabilities and a conservative detection profile, favoring specificity over sensitivity. Improvements in dataset diversity, labeling precision, and explainability may further enhance reliability for secondary caries detection. Clinical Significance: AI-based CDSSs assist clinicians by providing consistent detection. Their high specificity is particularly valuable in minimizing unnecessary invasive treatments (overtreatment), though they should be used as adjuncts rather than a replacement for expert judgment.

BackgroundPatients who have undergone Anterior Cruciate Ligament Reconstruction (ACLR) have a 6-24% chance of either re-tearing or having subsequent knee surgery. To date there have been no practical validated risk prediction models that can be easily implemented into clinical workflow for re-injury risk. Micro-Doppler radar (MDR) provides a promising solution. ObjectiveThe purpose of this study was to investigate the predictive ability of MDR to identify persons with a previous ACLR relative to an age and sex matched healthy control. MethodsACLR patients (n=81) and controls (n=100) performed drop box jump, sit to stand (STS), and walking trials as MDR signatures were collected. A 1D Convolutional Neural Network was developed to evaluate each activity individually followed by the development of a fusion model validation using all three activities. ResultsThe STS model individually achieved the highest overall accuracy of 82.3%, with a sensitivity of 71.6% and specificity of 91.0%. The fusion model using all activities achieved a peak overall accuracy to detect ACLR of 86.2%, 80.3% sensitivity, and 91% specificity. ConclusionsCurrently, there is no clinically validated, efficient approach to objectively evaluate human motion at the point of care. When coupled with machine learning, MDR accurately differentiates ACLR from control groups by identifying complex biomechanical asymmetries, with classification performance comparable to or exceeding that of motion capture. Future research is needed to determine if MDR can be used in conjunction with risk prediction modeling. Key pointsMicro-Doppler radar provides a promising new solution to identify important human motion asymmetries in clinical settings. Here we evaluated a group of patients who have a history of Anterior Cruciate Ligament reconstruction versus a control group. Simple movements performed in the presence of the micro-Doppler radar system were used to identify the 2 groups with accuracy comparable or superior to motion capture systems.

Background: Sub-Saharan Africa (SSA) still experiences a high burden of micronutrient deficiencies. For monitoring of micronutrient status among young children in SSA, non-invasive alternatives to blood-based biomarkers are desirable. Handheld Raman spectrophotometry appears to offer this alternative to quantify intracellular stores of micronutrients. In rural Burkina Faso and Kenya, we validated the Cell-/SO-Check device (ZellCheck(R)) against conventional laboratory-based methods. Methods: For this validation study, we recruited children aged [&ge;]24 months attending routine clinics within the Health and Demographic Surveillance Systems (HDSS) in Siaya and Nouna. Anthropometric measurements and venous blood samples were taken. Plasma ferritin, soluble transferrin receptor (sTfR) and C-reactive protein (CRP) were measured by ELISA, and plasma zinc by atom absorption. The spectrometer was used to quantify zinc and iron. For continuous outcomes, we generated Bland Altman plots and calculated bias and limits of agreement (LoA). For binary outcomes, we produced Receiver Operator Characteristic (ROC) areas under the curve (AUC), and estimated sensitivity, specificity and predictive values. Results: We analysed data of 48 children from Burkina Faso and 54 children from Kenya (male: 53%; age range: 24-66 months). According to spectrophotometry, the proportions of iron deficiency and zinc deficiency were 16.7% and 25.5%, respectively. The median concentrations were for ferritin 24.0 {micro}g/L (range: 2.0-330.0), for sTfR 5.7 mg/L (2.8-51.0), and for zinc 9.9 {micro}mol/L (5.2-25.0). The corresponding bias for iron levels by spectrophotometry was 42.4 with LoA: -18.7, 103.6. The bias for zinc levels was 7.5 with LoA: -49.3, 64.2. For the classification of deficiency, the ROC-AUC, sensitivity, and specificity for spectrophotometry vs. biomarker-based diagnosis were for iron deficiency 0.62, 68% and 55%, respectively, and for zinc deficiency 0.55, 33% and 91%, respectively. Conclusions: The Cell-/SO-Check device may be used to rank children in population-based studies in SSA according to their zinc status, but not iron status. The method should not replace the standard laboratory measurements for clinical diagnoses of zinc and iron deficiencies.

Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including label-free phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the level of downstream biological analysis remain limited. To address this gap, we evaluated several recent segmentation models, including Cellpose cyto3, Cellpose-SAM, {micro}SAM, and CellSAM, on phase contrast and fluorescence cell culture images. In addition, Mesmer and InstanSeg were included for benchmarking on multiplexed fluorescence tissue images generated using CO-Detection by IndEXing (CODEX). We found that Cellpose-SAM achieved strong performance on phase contrast images, while SAM-based models consistently performed well on fluorescence cell culture data. In contrast, no single model consistently outperformed others on CODEX datasets. Instead, each model exhibited distinct strengths and limitations, which led to differences in downstream analyses, including clustering and cell type identification. Together, our study emphasizes the importance of selecting segmentation models based on dataset characteristics and analytical goals, rather than relying on a single universal approach.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved since its emergence in the human population in 2019. As of 1st August 2025, more than 1,700 Omicron subvariants have been designated by the Pango nomenclature system. The Pango nomenclature system designates a new lineage based on genetic and epidemiological information of SARS-CoV-2 strains. However, there is a possibility that strains that have similar genetic backgrounds and the same phenotype are given different Pango lineage names. In this paper, we propose a new algorithm, called FindPart-w, which can identify groups of viral lineages that share the same relative effective reproduction numbers. We introduced a new lineage replacement model, called the constrained RelRe model, which constrains groups of lineages to have the same relative effective reproduction numbers. The FindPart-w algorithm searches the equality constraints that minimise the Akaike Information Criterion of constrained RelRe models. Using hypothetical observation count data created by simulation, we found that the FindPart-w algorithm can identify groups of lineages having the same relative effective reproduction number in a practical computational time. Applying FindPart-w to actual real-world data of time-stamped lineage counts from the United States, we found that the Pango lineage nomenclature system may have given different lineage names to SARS-CoV-2 strains even if they have the same relative effective reproduction number and similar genetic backgrounds. In conclusion, this study showed that viruses that had the same relative effective reproduction number were identifiable from temporal count data of viral sequences. These findings will contribute to the future development of lineage designation systems that consider both genetic backgrounds and transmissibilities of lineages.

Background: Diabetic retinopathy (DR) is the leading cause of preventable blindness in working-age adults. In Chile, despite GES coverage since 2006, screening reaches only ~21% of the diabetic population under control. Chilean evidence shows that autonomous AI screening platforms have produced heterogeneous field results (sensitivity 40.8-100%, specificity 55.4%), while Ophthalmic Medical Technologists (TMOs) consistently achieve >97% sensitivity, suggesting AI is most effective as structured support for trained professionals rather than as an autonomous filter. Objective: We present DIRD+ (Diabetic Integrated Retinal Diagnosis), an open-source clinical platform that performs complete DR clinical workflows - patient management, AI-assisted lesion detection, clinical classification, annotation, and report generation - entirely within the web browser using WebAssembly-based inference, without transmitting patient data to any server. This work describes the system architecture and a preliminary technical validation. Methods: DIRD+ implements a six-stage inference pipeline using ONNX Runtime Web (v1.23) with SIMD and multi-thread optimizations, a pluggable clinical guideline engine (ICDR 2024, MINSAL Chile 2017), and a human-in-the-loop annotation workflow. A YOLOv26n detection model was trained on 500 pseudo-labeled APTOS 2019 images using the Annotix framework [11] and evaluated on the IDRiD test set (n=81 images). Results: Optic disc detection - the spatial calibration landmark - achieved AP=1.000 on IDRiD (IoU=0.1). Soft exudate detection achieved AP=0.243 (F1=0.364). Internal validation mAP50=0.578. Browser-based inference averaged 0.297 s/image (3.4 images/second) on CPU without GPU. Lesion detection performance reflects a first-generation model trained on 500 images; progressive improvement through collaborative annotation is ongoing. Conclusions: DIRD+ demonstrates that a complete offline-first DR clinical workflow can be deployed at zero cost within a standard web browser without server infrastructure or GPU. The pluggable guideline engine and human-in-the-loop architecture make DIRD+ a viable tool for TMO-assisted screening in connectivity-limited primary care settings.

We investigate the effects of skin pigmentation and light source characteristics on the performance of reflective Pulse oximetry (PO) devices used in healthcare and well-being applications. We use Monte Carlo (MC) simulations to compare ideal monochromatic and realistic LED spectral emission profiles and tolerance-related wavelength shifts. The simulation covers photon transport in skin models with melanin concentrations (2.55% to 30.5%) and arterial oxygen saturations SaO2 (70% to 100%.) Accuracy was assessed by SpO2 error, root-mean-square error RMSE (Arms), and percentile tail-errors (P90, P95, and P99). Monochromatic spectral emission yielded the lowest SpO2 error (RMSE = 1.32), while LED spectral emission profiles increased errors (RMSE = 2.10). Infrared wavelength tolerances increased SpO2 RMSE by 1.1 {+/-} 0.3. SpO2 error increased with melanin concentration, from underestimation (-1.8 {+/-} 0.1%) at 2.55% melanin concentration to overestimation (+3.9 {+/-} 1.2%) at 30.5% for low SaO2 (70%) and LED spectral emission profiles. At 30.5% melanin concentration, P95 and P99 exceeded FDA and DIN EN ISO 80601-2-61 thresholds, in particular at low SaO2 (70%). Clipping SpO2 estimates at 100% resulted in an apparent RMSE decrease of up to 3%, reflecting error masking rather than real error reduction. In conclusion, LED spectral emission profiles and wavelength tolerances can amplify melanin-related bias in SpO2 estimates. Monochromatic emission and tighter wavelength control can reduce SpO2 error and should be considered in device design and regulation. Regulatory standards should discourage clipping SpO2 estimates at 100% and mandate additional metrics as RMSE fails to reflect clinically critical percentile error thresholds, i.e. P95 and P99.