MethodsX

AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition "fitness" score, then validated its utility in two external populations. Methods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition "fitness" score. We then assessed the associations of "fitness" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition "fitness" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition "fitness" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001). ConclusionsWe demonstrated that a comprehensive body composition "fitness" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed. Graphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition "fitness" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/26352573v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1b98c3eorg.highwire.dtl.DTLVardef@a64282org.highwire.dtl.DTLVardef@1589559org.highwire.dtl.DTLVardef@b5423f_HPS_FORMAT_FIGEXP M_FIG C_FIG

IntroductionMagnetic resonance imaging (MRI) is central to neurological care, yet access remains profoundly inequitable in low- and middle-income countries, especially in rural health facilities where high costs and fragile electricity supply limit services. Ultra-low-field (ULF) portable MRI offers a way to expand access, but deployment in weak-grid settings requires robust affordable power. We characterized the power needs of a 0.064T portable ULF MRI system and assessed the feasibility of a solar-powered MRI-capable facility in a rural Zimbabwean clinic, which we believe to be the first of its kind in the world. MethodsWe measured the power draw of an ultra-low-field MRI session from a portable photovoltaic (PV) battery kit in the UK, quantifying scan, standby and energy use. We then monitored a PV-battery micro-grid supplying a protected circuit at an MRI-capable clinic in Shurugwi, Zimbabwe. Inverter telemetry was used to derive PV generation, load, battery state of charge (SoC) and grid import for working days in October-November 2025, spanning the end of the dry season and onset of the rainy season. ResultsIn the portable configuration, a 64-minute MRI session consumed [~]0.21 kWh, with standby demand of [~]1.44 kWh per 24 hours. In clinic, mean PV generation was 9.10 kWh (SD=1.34) and load 9.91 kWh, with zero recorded grid import and minimum daily SoC typically [&ge;]60%, including during the early rainy season. ConclusionAn affordable PV-battery micro-grid can reliably support ULF MRI and associated research power loads in a rural, weak-grid clinic, offering a reproducible blueprint to narrow diagnostic equity gaps in resource-limited settings.

Investigating single-cell dynamics and morphology in tissues and embryos requires highly accurate quantitative analysis of microscopy images. Despite significant advances in the field of bioimage analysis, even the most sophisticated segmentation and tracking algorithms inevitably produce errors (e.g. : over segmentation, missing objects, miss-connected objects). Although error rate may be small, their propagation throughout a time-lapse sequence has catastrophic effects on the accuracy of tracking and extraction of single cell parameters. Extracting single cell temporal information in the context of tissue/embryo requires thus expert curation to identify and correct segmentation errors. In the movies commonly used in developmental biology and stem cell research, both the number of imaged cells and the duration of recording are large, making this manual correction task extremely time-consuming. This has now become a major bottleneck in the fields of development, stem cell biology and bioimage analysis. We present here EpiCure (Epithelial Curation), a versatile tool designed to streamline and accelerate manual curation of segmentation and tracking in 2D movies of large epithelial tissues. EpiCure uses temporal information and morphometric parameters to automatically identify segmentation and tracking errors and provides user-friendly tools to correct them. It focuses on ergonomics and offers several visualization options to help navigating in movies of tissue covering a large number of cells, speeding up the detection of errors and their curation. EpiCure is highly interoperable and supports input from a wide range of segmentation tools. It also includes multiple export filters, enabling seamless integration with downstream analysis pipelines. In this paper, using movies from several animal models, we highlight the importance of curating cell segmentation and tracking for accurate downstream analysis, and demonstrate how EpiCure helps the curation process for extracting accurate single cell dynamics and cellular events detection, making it faster and amenable on large dataset.

AimsThe development of a method for isolating mitochondria from a specific cell type within a given tissue, while preserving their structural and functional integrity to the greatest possible extent, remains an ongoing challenge. The aim of this study was to establish a protocol for the isolation of mitochondria from rodent cardiomyocytes, characterized by minimal contamination with other cell types and a high yield of mitochondrial fractions originating from distinct subcellular regions of cardiomyocytes. Methods and resultsIn the present study, cardiomyocytes from guinea pig and rat hearts were isolated using a standard enzymatic digestion protocol in a Langendorff heart perfusion system. Traditionally, the isolation of organelles, including mitochondria, from whole cardiac tissue as well as from cardiomyocytes has relied primarily on mechanical tissue homogenization These conventional approaches involve the localized application of high pressure to cells, which may potentially damage delicate organelles, particularly mitochondria. Moreover, such homogenization preferentially releases mitochondria located in the subsarcolemmal region of cardiomyocytes rather than representing the entire mitochondrial population. In our study, we employed an alternative approach based on the gentle mechanical disruption of cardiomyocytes by passing the cell suspension through selected cell strainers using a cell scraper. This strategy facilitated mild disruption of cellular structures, significantly increasing the yield of mitochondria released from interfibrillar regions while preserving mitochondrial functionality. Moreover, this method decrease probability of sample contamination with mitochondria from other cells, based on cell size differences. The effectiveness of this method was confirmed by transmission electron microscopy, and high-resolution respirometry, which revealed no evidence of outer mitochondrial membrane damage, as indicated by the lack of response to the addition of exogenous cytochrome c to the incubation chamber. Moreover, mitochondrial oxygen consumption increased by 7.39 {+/-} 1.25-fold following the addition of 100 {micro}M ADP, reflecting efficient ADP-stimulated respiration. Furthermore, fluorescence measurements were performed. to assess changes in the mitochondrial inner membrane potential ({Delta}{Psi}). The isolated mitochondria were also suitable for electrophysiological studies using the single-channel patch-clamp technique. Additionally, mitochondria isolated using the protocol developed in our laboratory exhibited a high capacity for transplantation into H9c2 cells. ConclusionIn summary, our mitochondrial isolation method is rapid, efficient, and yields functionally competent mitochondria. These preparations are suitable for a wide range of downstream applications, including patch-clamp electrophysiology, analyses of oxygen consumption under various pharmacological conditions, as well as mitochondrial transplantation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=162 HEIGHT=200 SRC="FIGDIR/small/716092v1_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@613495org.highwire.dtl.DTLVardef@1c34338org.highwire.dtl.DTLVardef@722900org.highwire.dtl.DTLVardef@e1f7a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

Objectives: To determine the impact of discharge home on physical activity and sedentary behaviour following orthopaedic trauma. Design: Observational study. Setting: Acute hospital. Participants: Between October 2022 and January 2024, 31 adult orthopaedic trauma patients were recruited during hospital admission. Participants had either an isolated hip fracture or multi-trauma (i.e., a lower limb fracture, with an upper limb and/or spinal fracture). Interventions: Participants wore two activity monitors (activPAL3 and ActiGraphGT3x) during the final days of an acute hospital admission and the first five days at home. An interrupted time series analysis evaluated changes physical activity variables during the hospital to home transition. Participants were analysed individually using mixed-effects linear regression allowing the intercept to vary by participant. Main outcome measures: Primary outcome was daily steps; secondary outcomes included sedentary time and other activity measures. Results: Daily steps (mean +- SD) were higher at home (4552.4 +- 2639.5) compared to hospital (2597.8 +- 1450.8). Modelled results indicated a 27% increase in daily steps following hospital discharge (exp(beta946;): 1.27, 95% CI: 1.01,1.59, p=0.039) and a sustained improvement at home. No significant differences were observed between hip fracture and multi-trauma participants. Conclusion: Participants recovering from orthopaedic trauma showed a significant increase in daily step count upon discharge home from hospital, highlighting the positive impact of the home environment on activity levels. Further research is warranted to assess the effectiveness of interventions to improve activity levels in hospital (e.g., early intensive therapy) and at home (e.g., immediate home-based physiotherapy) in individuals following orthopaedic trauma.

As toxicology shifts towards non-animal testing, quantitative models are essential to predict adverse health effects from molecular or cellular perturbations. Quantitative Adverse Outcome Pathways (qAOPs) represent such models, building on mechanistic knowledge and quantifying the Key Event Relationships (KERs) described in AOPs. Despite the recognized need, the number of qAOPs remains limited. Bayesian-based approaches are often chosen for developing qAOP for their flexibility, but most use discretized variables, limiting their predictive power. In addition, these models are mainly built from newly generated data, underexploiting the large amount of information available. This study successfully leverages data from public literature and presents an innovative framework based on continuous variables to develop a Bayesian-based quantitative model for a central KER towards liver fibrosis. The model predicts the probability of the expression fold change for two key markers of hepatic stellate cell activation (aSMA and COL1A1), given the effects on tissue injury, using in vitro data from 9 chemicals. We propose a newly developed workflow to assist in knowledge identification, organization, and extraction from scientific literature and chemical databases. Based on in vitro data and in vivo information from the Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) database, we estimate a biologically relevant range in COL1A1 fold change that indicates an activated state of stellate cells and high liver fibrosis odds ratios. Our study provides a case example of integrating published data and continuous variables to build a Bayesian-based model, which constitutes an essential step for predicting liver fibrosis from in vitro data.

1Sex steroid hormones are not exclusively localised in the circulation and can be found in numerous extragonadal tissues, in concentrations unrelated to the circulating fraction. Existing methodology to measure intramuscular steroid hormone concentrations includes both immune-based assays and liquid chromatography-mass spectrometry (LC-MS), the gold standard for hormone measurements. To date, no LC-MS based methods validation has been published on the measurement of intramuscular sex steroid hormones, despite clear biological relevance. Here, we describe the development and validation of a simple, high-throughput LC-MS Orbitrap method for the measurement of 10 intramuscular sex steroid hormones, including pregnenolone, progesterone, dehydroepiandrosterone, androstenedione, testosterone, epitestosterone, dihydrotestosterone, oestrone, oestradiol, and oestriol. In brief, isotope labelled standards were added to 5-6 milligrams of lyophilised muscle tissue, homogenised and extracted with ethyl acetate. The extracts were dried down and sequentially derivatised with 1-methylimidazole-2-sulfonyl chloride and hydroxylamine hydrochloride to target both the phenolic hydroxyl groups and ketone groups. The limit of detection was 1.0 {+/-} 1.0 pg/mg (range 0.36 - 3.26 pg/mg), with a R2 > 0.99 for all analytes. Matrix effects were 90-110% for all analytes except for dihydrotestosterone (143.6%), and precision was <10 CV% for all analytes in the presence of a muscle matrix. Our method allows for 20-40 samples to be prepared in [~]4 h, with a sample data acquisition time of 13 minutes. Moreover, our method provides the opportunity for specific analysis of steroid hormone concentrations in skeletal muscle, allowing target tissue specificity instead of relying on proxy measures from the circulation.

Transcriptional bursts regulate gene expression by altering burst size or burst frequency. Here, we present a protocol that integrates fixed-cell smFISH and live-cell single-molecule imaging to analyze estrogen-responsive transcriptional bursting of the TFF1 gene in human breast cancer cell lines. This workflow enables measurement of burst size, burst initiation, and active allele frequency to determine how endocrine disruptor chemicals modulate transcriptional bursting dynamics. For complete details on the use and execution of this protocol, please refer to Day, Yasar et al.1

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non-overlapped configurations specifically for Tc 99m MPI - parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSCs superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc-99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

Quality and price of fetal bovine serum (FBS) are traditionally determined by geographical origin and parameters listed in the Certificate of Analysis (CoA). Despite its central role in cell culture, selecting suitable FBS batches remains costly and labor-intensive due to substantial batch-to-batch variation. We propose a molecular assessment strategy based on transcriptomic and cytokine profiling of cells cultured in different FBS batches to evaluate performance more reliably. Analysis of differential gene expression in three cell lines - MRC-5, Jurkat, and THP-1 - enables batch grouping and reveals pathway-specific effects, with immune-related pathways showing the most pronounced variability. Although CoA parameters can stratify batches by origin, they do not consistently correlate with cytokine secretion or gene expression across cell lines. These findings demonstrate that geographical origin is an inadequate predictor of functional FBS performance and that molecular profiling provides a more robust and informative assessment.

Genetically encoded biosensors are GFP-based tools that can visualize the dynamics and spatial features of cellular processes. The design of a genetically encoded biosensor dictates the method that is used to measure the response. Common read-outs use some sort of fluorescence intensity measurement, which is subject to both technical and biological perturbations, including sample drift, excitation power fluctuations, changes in sample size/volume, or a change in expression level. Yet, the fluorescence lifetime of a fluorophore is not affected by the aforementioned perturbations. Therefore, biosensors that respond with a large lifetime change offer a more robust method of detecting cellular processes. Here, we report on protocols for calcium imaging using fluorescence lifetime imaging microscopy (FLIM) to measure the response of a genetically encoded lifetime biosensor. The protocols include details on biosensor production and purification, calibration of purified biosensor with FLIM, introduction of the plasmid in HeLa and endothelial cells, and timelapse analysis of FLIM data. In this chapter we use the green fluorescent biosensor G-Ca-FLITS as an example but the protocols can be generally applied to biosensors with lifetime contrast. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/717680v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@167f612org.highwire.dtl.DTLVardef@4c5603org.highwire.dtl.DTLVardef@1a2eb6borg.highwire.dtl.DTLVardef@10ddc63_HPS_FORMAT_FIGEXP M_FIG C_FIG

Until now, there has been no animal-free alternative method for predicting chronic inflammation and delivering the associated dose responses, the timing of onset, and the duration of inflammation, as required by regulatory agencies. We present the results of pre-validation of an in-vitro-learned digital twin (InFiniteLungDT) capable of predicting chronic neutrophilic lung inflammation for regulatory use. The method is based on measuring the dynamics of early biological effects in vitro induced by respirable materials or their mixtures, without the need to know their intrinsic properties. We constructed the digital twin(s) for each of the material, for which we have in vivo exposure data. The instillation data set, comprising 49 different nanomaterials, was used as the primary anchor to calibrate the model. Inhalation data set, comprising 7 different nanomaterials, compliant with OECD TG 412, was used to show the general applicability of the method across species and for different exposure scenaria. In total, about 3094 single mouse exposures and 364 rat exposures (and approx. 775/225 non-exposed mouse/rat controls) were used to predict concentration-dependent time-evolved neutrophil influx into the lung. The accuracy (predictive capacity) of LOAEL determination is 93% for instillation and 84% for inhalation exposure. Taking into account the time-to-deliver-result being less than 1 week, this proves that the effect of inhaled material from acute to chronic conditions can be assessed orders of magnitude faster and cheaper than in a reference animal study.

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two large-scale proteomics datasets generated from mouse dorsal skin punch samples collected following controlled X-ray exposures spanning multiple doses, dose rates, and post-exposure time points. Experiment 1 comprised 96 samples (including 16 reference samples) collected 6 days after exposure to 0-75 cGy delivered at either 30 or 300 cGy/min. Experiment 2 comprised 936 samples (including 236 reference samples) exposed to 0-100 cGy at either 3 or 28 cGy/min dose rates and harvested between 7 and 150 days post-exposure. All samples were processed using a standardized workflow involving automated bead-based digestion and data-independent acquisition mass spectrometry. The datasets include multiple pooled reference sample types, process controls, and system suitability standards ensuring high quality data. All data presented are available via ProteomeXchange at several levels of processing, from raw files through normalized peptide- and protein-level abundance matrices suitable for biomarker discovery and machine learning applications. This dataset will facilitate generation of new insights into the biological changes and molecular signatures resulting from X-ray exposure in mice and may also help inform future studies in humans.

Aims: Explainable advanced electrocardiography (A-ECG) can be used to estimate heart age from the standard 12-lead ECG. A-ECG heart age gap (HAG) represents the difference between A-ECG heart age and chronological age. Increased A-ECG HAG is associated with cardiovascular outcomes and can be used to communicate risk. The aim was to investigate whether A-ECG heart age demonstrates acceptable within- and between-session reproducibility. Methods: Healthy adults (n=42, age 23+/-4 years, 52% male) attended up to two sessions ~14 days apart, with 36 participants completing both sessions. During each session, five standard resting 12-lead ECGs were obtained while lying in the supine position with unchanged electrode positions. A-ECG heart age was extracted using dedicated software. Within-session reproducibility was assessed using all five recorded ECGs with coefficient of variation (CV) and a two-way random effects intraclass correlation coefficient (ICC). Between-session reproducibility was assessed using the first recorded ECG of each session with a paired t-test, CV and ICC. A further analysis assessed the reproducibility of the parameters used in the A-ECG heart age regression model. Results: A-ECG heart age showed excellent within-session reproducibility in session one and two (both CV 5.8%, ICC 0.99). A-ECG heart age was slightly lower in session one than two (24.0+/-7.5 vs. 25.5+/-7.8 years, p=0.04) and showed good between-session reproducibility (CV 8.3%, ICC 0.84). All but one parameter used to estimate A-ECG heart age showed acceptable within- and between-session reproducibility (CV<10%). Conclusion: A-ECG heart age demonstrates excellent within-session reproducibility and good between-session reproducibility in healthy young adults.

In vitro cytotoxicity assessments frequently rely on staining-based methods that indirectly estimate viable cell numbers indirectly. A major limitation of many such techniques is their endpoint nature, requiring cell lysis or irreversible processing that precludes longitudinal monitoring of cellular responses following treatment. An ideal assay for evaluating cell viability and proliferation should be simple, rapid, cost-effective, reproducible, and highly sensitive, while also enabling accurate quantification with minimal interference from test compounds. The resazurin reduction assay satisfies these criteria, offering a sensitive and economical alternative to conventional tetrazolium-based methods. Although both assay types depend on the metabolic reduction of a dye by viable cells, they differ mechanistically. Tetrazolium salts (e.g., MTT) are reduced by cellular dehydrogenases to insoluble formazan crystals that require solubilization before to detection. In contrast, resazurin--a cell-permeable, non-fluorescent blue dye--is reduced to resorufin, a highly fluorescent compound detectable without additional processing steps. This property renders the resazurin assay broadly applicable to viability testing in eukaryotic cells cultured in both 2D and 3D formats, as well as in bacterial systems. Here, we present a streamlined, universal protocol for implementing the resazurin reduction assay across diverse experimental models, emphasizing its practicality, reproducibility, and adaptability for real-time viability monitoring. Key featuresO_LIReal-time, non-destructive monitoring: Enables longitudinal studies by allowing repeated measurements of the same samples over hours without toxicity or disruption. C_LIO_LIStreamlined workflow: A simple "add-incubate-read" protocol eliminates the need for cell lysis, washing, or extraction, saving time and reducing variability. C_LIO_LIBroad sample compatibility: Versatile and reliable for use with 2D monolayers, 3D spheroids, organoids, and bacterial cultures. C_LIO_LIHigh sensitivity: Fluorescent detection of resorufin provides exceptional sensitivity, enabling accurate quantification of even small viable cell populations. C_LIO_LILow background and minimal interference: A clean fluorescent readout reduces the risk of signal artifacts, offering a more reliable alternative to traditional colorimetric assays. C_LIO_LICost-effective and accessible: Utilizes standard laboratory plate readers and commercially available reagents, making it an economical choice for any lab. C_LIO_LIScalable for high-throughput screening: Easily adaptable to various plate formats, supporting both small-scale experiments and large-scale automated screening applications. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/718248v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@82bcecorg.highwire.dtl.DTLVardef@14164aforg.highwire.dtl.DTLVardef@395118org.highwire.dtl.DTLVardef@fb1349_HPS_FORMAT_FIGEXP M_FIG C_FIG

The challenge of stratifying patients for combination therapy is both technically demanding and clinically crucial. In previous work, we introduced a multi-objective optimization framework for identifying optimally synergistic combination protocols that are robust to competing definitions of additivity. This manuscript extends this methodology to quantify how inter-individual variability in drug sensitivity influences the combination doses that optimally balance the competing objectives of synergy of efficacy and synergy of potency (a proxy measure of toxicity). For this methodology, we introduce a voxel-based stratification approach to characterize individuals (model parameterizations) into subgroups based on sensitivity to each drug as a monotherapy and in combination. As a case study, we apply the method to a preclinical dataset of murine response to the combination of an immune checkpoint inhibitor and an antiangiogenic agent. We demonstrate that the algorithm can quantify how the robustly optimal combination therapies vary across different treatment response subgroups and how the algorithm can identify subpopulations for which no meaningfully efficacious combination exists. As applying the methodology requires knowledge of specific parameter values for which measurable biomarkers may be unavailable, we also propose an initiation protocol that permits identification of the parameters necessary to place an individual in a subgroup. This methodology is a step in the direction of determining the right combination therapy for a subgroup and finding the right subgroup for an existing therapy.

Global healthcare is targeting patient-centred care, as it leads to better health outcomes and higher level of patient satisfaction. Patient-centred communication, is an important part of patient-centred care because it focuses on involving patients in their care. Recent surveys both nationally and globally have shown that patients are not involved enough in their own healthcare decisions. This problem is especially common among the elderly with chronic conditions. This study aimed to describe patient-healthcare professional interactions, expectations, and satisfaction in physiotherapy within an understudied context, thereby providing important, specific data on ICE dynamics and satisfaction in the specific setting. Cross-sectional study of participants in scheduled consultations was conducted. Two government physiotherapy centres, seven private physiotherapy centres and two trust centres with physiotherapy facilities in Gujarat, India. 232 patients (from various public and private physiotherapy clinics) participated in the study. Patients' ideas, concerns, expectations (ICE) and satisfaction were explored. Almost 88% of patients reported their thoughts and explanations about their symptoms during the consultation. Most patients described not having any concerns about the diagnosis/treatment, and more than two-third of patients consulting PTs expected explanation for their symptoms. Almost 90% patients were satisfied with the consultation. The study revealed that while most patients conveyed their thoughts during consultations, very few expressed their concerns. Overall, patients were satisfied with their consultations.

Recent progress in mathematical kinship modelling has allowed one to predict the probable numbers of kin for a typical population member. In the models, kin may be structured by age and sex, both in static or time-variant demographies. Knowing the probable numbers of kin in different stages - such as parity, health status, or geographic location - however, remains an open challenge in Kinship Demography. Knowing how population structure delimits kin to distinct stages is an advance - for instance, the probability of having one sister at home and one sister away has different social implications from the probability of having two sisters. We present a novel analytical framework, grounded in branching process theory, that provides kin-number distributions jointly structured by age and stage. Using recursive compositions of probability generating functions (PGFs), we derive the joint age, stage, and age x stage kin-number distributions. All marginal distributions over either dimension naturally emerge. Simple extensions of the PGF approach additionally yield: the joint distribution of an individuals own stage and their kins stage; the probable numbers of kin deaths, both in total and by generation number; and the probabilities of being kinless and/or orphaned. We demonstrate the framework through novel results in an application using UK parity-specific fertility and mortality data. HighlightsO_LIA new method calculates probability generating functions for the number of kin structured by age and stage C_LIO_LIThe model allows predicting the probable numbers of kin organised by age and stage C_LIO_LIRecursive nesting of probability generating functions in branching processes is used C_LIO_LIAn application is presented highlighting the novel results C_LI

The protein sample preparation methods for shotgun proteomics are nowadays well-established unlike the ones for whole protein analysis. The goal of my work has been to create a simple methodology which provides a single uncomplicated sample preparation tool for these two fields. Nowadays the bulk of proteomics work is done using detergents for protein solubilization. The presented concept, which is based on unspecific adsorption of protein molecules on wide pore materials, allows for protein capture and clean-up from solutions of the most commonly used sodium dodecyl sulfate detergent. It could also be applied to proteins in detergent-free solutions. After the capture and clean-up, proteins could be either cleaved for the downstream peptide analysis or eluted for the whole protein analysis. If required, the eluted whole proteins could be recaptured and cleaved into peptides. Depending on the experimental goals, the sample preparation device could be fitted with embedded proteolytic enzymes to simplify routine sample processing and/or reversed phase media for the downstream peptide or protein separation.

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;