The influence of cooling on biomechanical time since death estimations using ovine brain tissue.

The significance of biomechanical analyses for forensic time since death estimations has recently been demonstrated. Previous biomechanical analyses successfully discriminated post-mortem brain tissue from tissue with a post-mortem interval of at least one day when held at 20 °C. However, the practical utility of such analyses beyond day one at 20 °C was limited. This study investigates the storage, loss, and complex shear modulus of various brain regions in sheep stored at 4 °C in 24-hour intervals over four days post-mortem using rheometry tests. The aim is to identify the critical biomechanical tissue property values to predict post-mortem time and assess the temperature sensitivity of the rheometry method by comparing results to recent findings at 20 °C. Thirty sheep brains were examined, including the frontal lobe, parietal lobe, anterior and posterior deep brain, superior colliculi, pons, medulla, and cerebellum. Rheometry tests were conducted, and receiver operator characteristic analyses were employed to establish cut-off values. At 4 °C storage, all investigated biomechanical properties of the examined brain regions remained stable for at least one day post-mortem. Using cerebellar samples stored at 4 °C, a post-mortem interval of at least two days could be determined with excellent diagnostic ability. Complex shear modulus values below 1435 Pa or storage modulus values below 1313 Pa allowed prediction of two or more days post-mortem. Comparisons between 4 °C and 20 °C revealed brain region-specific results. For instance, the complex shear moduli of the anterior deep brain at 4 °C were significantly higher on all individual testing days when compared to 20 °C. In contrast, the combined medulla and pons samples were similar on each day. Rheometry testing of brain tissue consistently stored at 4 °C since death proved valuable for forensic time since death estimations starting from two days after death.

Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

A Novel Experimental Approach for the Measurement of Vibration-Induced Changes in the Rheological Properties of Ex Vivo Ovine Brain Tissue.

Increased incidence of traumatic brain injury (TBI) imposes a growing need to understand the pathology of brain trauma. A correlation between the incidence of multiple brain traumas and rates of behavioural and cognitive deficiencies has been identified amongst people that experienced multiple TBI events. Mechanically, repetitive TBIs may affect brain tissue in a similar way to cyclic loading. Hence, the potential susceptibility of brain tissue to mechanical fatigue is of interest. Although temporal changes in ovine brain tissue viscoelasticity and biological fatigue of other tissues such as tendons and arteries have been investigated, no methodology currently exists to cyclically load ex vivo brain tissue. A novel rheology-based approach found a consistent, initial stiffening response of the brain tissue before a notable softening when subjected to a subsequential cyclic rotational shear. History dependence of the mechanical properties of brain tissue indicates susceptibility to mechanical fatigue. Results from this investigation increase understanding of the fatigue properties of brain tissue and could be used to strengthen therapy and prevention of TBI, or computational models of repetitive head injuries.

The use of brain tissue mechanics for time since death estimations.

Time since death estimation is a vital part of forensic pathology. Despite the known tissue degradation after death, the efficacy of using biomechanical tissue properties to estimate time since death remains unexplored. Here, eight brain tissue localizations were sampled from the frontal lobe, parietal lobe, anterior and posterior deep brain, superior colliculi, pons, medulla, and cerebellum of 30 sheep; were then stored at 20 °C; and subsequently subjected to rheometry tests on days zero to four after death. Overall, the measured tissue storage modulus, loss modulus, and complex shear modulus decreased after death for all of the tested regions in a site-specific manner. Day zero to day one changes were the only 24-h interval, for which statistically significant differences in tissue mechanical moduli were observed for some of the tested brain regions. Based on receiver operator characteristic analyses between day zero and the pooled data of days one to four, a post mortem interval of at least 1 day can be determined with a sensitivity of 90%, a specificity of 92%, and a positive likelihood ratio of 10.8 using a complex shear modulus cut-off value of 1461 Pa for cerebellar samples. In summary, biomechanical properties of brain tissue can discriminate between fresh and at least 1-day-old samples stored at 20 °C with high diagnostic accuracy. This supports the possible value of biomechanical analyses for forensic time since death estimations. A striking advantage over established methods to estimate the time since death is its usability in cases of disintegrated bodies, e.g. when just the head is found.

Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals.

Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations-such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.

Laparoscopic Video Analysis Using Temporal, Attention, and Multi-Feature Fusion Based-Approaches.

Adapting intelligent context-aware systems (CAS) to future operating rooms (OR) aims to improve situational awareness and provide surgical decision support systems to medical teams. CAS analyzes data streams from available devices during surgery and communicates real-time knowledge to clinicians. Indeed, recent advances in computer vision and machine learning, particularly deep learning, paved the way for extensive research to develop CAS. In this work, a deep learning approach for analyzing laparoscopic videos for surgical phase recognition, tool classification, and weakly-supervised tool localization in laparoscopic videos was proposed. The ResNet-50 convolutional neural network (CNN) architecture was adapted by adding attention modules and fusing features from multiple stages to generate better-focused, generalized, and well-representative features. Then, a multi-map convolutional layer followed by tool-wise and spatial pooling operations was utilized to perform tool localization and generate tool presence confidences. Finally, the long short-term memory (LSTM) network was employed to model temporal information and perform tool classification and phase recognition. The proposed approach was evaluated on the Cholec80 dataset. The experimental results (i.e., 88.5% and 89.0% mean precision and recall for phase recognition, respectively, 95.6% mean average precision for tool presence detection, and a 70.1% F1-score for tool localization) demonstrated the ability of the model to learn discriminative features for all tasks. The performances revealed the importance of integrating attention modules and multi-stage feature fusion for more robust and precise detection of surgical phases and tools.

Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression.

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.

Particle Image Velocimetry Evaluation of Hemodynamics Proximal to the Kissing Stent Configuration in the Aorto-Iliac Bifurcation.

PURPOSE: The kissing stent (KS) method is low-risk compared with open surgery techniques. It is often used to treat aorto-iliac occlusive disease (AIOD). Deployment of the KS geometry has a high technical success rate. However, stent patency reduces in the first 5 years potentially due to deleterious flow behavior. Potentially harmful hemodynamics due to the KS were investigated in vitro. METHODOLOGY: A compliant phantom of the aorto-iliac bifurcation was manufactured. Two surrogate stent-grafts were deployed into the phantom in the KS configuration to investigate effects of the presence of the stents, including the compliance mismatch they cause, on the hemodynamics proximal and distal to the KS. The investigation used pulsatile flow through a flow circuit to simulate abdominal aortic flow. Particle image velocimetry (PIV) was used to quantify the hemodynamics. RESULTS: PIV identified peak proximal and distal velocity in vitro was 0.71 and 1.40m·s-1, respectively, which were within physiological ranges. Throughout systole, flow appeared normal and undisturbed. A lumen wall collapse in the sagittal plane formed during late systole and continued to early diastole proximal to the aorto-iliac bifurcation, distal to the inlet stent position. The wall collapse led to disturbed flow proximal to the stented region in early diastole producing potential recirculation zones and abnormal flow patterns. CONCLUSION: The normal systolic flow behavior indicates the KS configuration is unlikely to cause an inflammatory response of the arterial walls. The collapse has not been previously identified and may potentially cause long-term patency reduction. It requires further investigation. CLINICAL IMPACT: The role of this article is to provide further insight into the haemodynamic behavior through a stented aorto-iliac artery. The results of this investigation will improve the understanding of the effects that using the kissing stent method may have on a patient and help to identify high risk regions that may require more detailed monitoring. This paper also develops the in vitro modelling techniques that will enable further research that cannot be carried out within patients.

Review of the Development of Hemodynamic Modeling Techniques to Capture Flow Behavior in Arteries Affected by Aneurysm, Atherosclerosis, and Stenting.

Cardiovascular diseases (CVDs) are the leading cause of death in the developed world. CVD can include atherosclerosis, aneurysm, dissection, or occlusion of the main arteries. Many CVDs are caused by unhealthy hemodynamics. Some CVDs can be treated with the implantation of stents and stent grafts. Investigations have been carried out to understand the effects of stents and stent grafts have on arteries and the hemodynamic changes post-treatment. Numerous studies on stent hemodynamics have been carried out using computational fluid dynamics (CFD) which has yielded significant insight into the effect of stent mesh design on near-wall blood flow and improving hemodynamics. Particle image velocimetry (PIV) has also been used to capture behavior of fluids that mimic physiological hemodynamics. However, PIV studies have largely been restricted to unstented models or intra-aneurysmal flow rather than peri or distal stent flow behaviors. PIV has been used both as a standalone measurement method and as a comparison to validate the CFD studies. This article reviews the successes and limitations of CFD and PIV-based modeling methods used to investigate the hemodynamic effects of stents. The review includes an overview of physiology and relevant mechanics of arteries as well as consideration of boundary conditions and the working fluids used to simulate blood for each modeling method along with the benefits and limitations introduced.

Frequency and duration of ambulance officer exposure to nitrous oxide and methoxyflurane in New Zealand.

OBJECTIVE: Nitrous oxide (Entonox®) and methoxyflurane (Penthrox®) are inhaled analgesics administered in paramedicine. Occupational exposure to nitrous oxide has been associated with negative health effects, and may inhibit professional capability. The effect of occupational exposure to methoxyflurane has not yet been clearly determined. This study identifies the frequency and duration of ambulance officer (AO) occupational exposure to nitrous oxide and methoxyflurane to provide a foundation for future assessments of occupational toxicity risk. METHODS: A retrospective database review of Patient Report Forms (PRFs) in 11 months between February 2016 and February 2018 was conducted. Nitrous oxide was available for the first 5 months studied, followed by 6 months methoxyflurane availability. AO-specific measures of attendance, rate of inhaled analgesic use, and duration of analgesic use were determined. Subgroup analysis by AO qualification and rostered work hours was undertaken. RESULTS: A total of 46,759 PRFs were examined, identifying 1,033 cases of nitrous oxide administration and 1456 cases of methoxyflurane was administration. There was a significant increase in the proportion of cases where inhaled analgesia was administered following the replacement of nitrous oxide with methoxyflurane. Relative risk of exposure to methoxyflurane compared with nitrous oxide was 1.22, while median duration of each exposure remained unchanged (32 vs. 33 min). CONCLUSIONS: Methoxyflurane via the Penthrox® inhaler was more likely to be administered than nitrous oxide. Most AOs are infrequently exposed to inhaled analgesics and are exposed for durations slightly greater than previously reported. Relative risk of exposure was greatest for lower-qualified AOs. Peak number of exposures and duration values suggest a subset of AOs with higher occupational health risk.

Optimising mechanical ventilation through model-based methods and automation.

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics.

BACKGROUND: For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. METHODS AND RESULTS: Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH2O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH2O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). CONCLUSIONS: Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.

Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart.

Effective mathematical modelling of continuous subcutaneous infusion pharmacokinetics should aid understanding and control in insulin therapy. Thorough analysis of candidate model performance is important for selecting the appropriate models. Eight candidate models for insulin pharmacokinetics included a range of modelled behaviours, parameters and complexity. The models were compared using clinical data from subjects with type 1 diabetes with continuous subcutaneous insulin infusion. Performance of the models was compared through several analyses: R2 for goodness of fit; the Akaike Information Criterion; a bootstrap analysis for practical identifiability; a simulation exercise for predictability. The simplest model fit poorly to the data (R2 = 0.53), had the highest Akaike score, and worst prediction. Goodness of fit improved with increasing model complexity (R2 = 0.85-0.92) but Akaike scores were similar for these models. Complexity increased practical non-identifiability, where small changes in the dataset caused large variation (CV > 10%) in identified parameters in the most complex models. Best prediction was achieved in a relatively simple model. Some model complexity was necessary to achieve good data fit but further complexity introduced practical non-identifiability and worsened prediction capability. The best model used two linear subcutaneous compartments, an interstitial and plasma compartment, and two identified variables for interstitial clearance and subcutaneous transfer rate. This model had optimal performance trade-off with reasonable fit (R2 = 0.85) and parameterisation, and best prediction and practical identifiability (CV < 2%).

The necessity of identifying the basal glucose set-point in the IVGTT for patients with Type 2 Diabetes.

BACKGROUND: The model-based dynamic insulin sensitivity and secretion test (DISST) uses fasting glucose (G 0 ) as the basal glucose (G B ) concentration when assessing insulin sensitivity (SI). However, this model was developed in a healthy, normoglycaemic cohort. We sought to determine the suitability the DISST model has for individuals with established type 2 diabetes (T2D). METHODS: 14 participants with established T2D were recruited to take part in a dietary intervention study. Insulin-modified intravenous glucose tolerance tests (IM-IVGTT) were undertaken at week 0, 12 and 24 and were used with DISST model to identify G B . A total of 36 tests were conducted across 12 participants throughout the study. Measured G 0 and identified G B values were compared using a Kolmogorov-Smirnov (KS) and signed rank (RS) test for the cohort. RESULTS: There were significant differences between the G 0 and identified G B values in this cohort (prs and pks < 0.0001), although both values were well correlated (R = 0.70). The residual plot demonstrates that the modified model captures the behaviour of the participants more accurately than the original model. CONCLUSIONS: This analysis has shown that G B is an important variable for modelling the glycaemic behaviour in T2D. These findings suggest that the original DISST model, while appropriate for normoglycaemic cohorts, needs to model basal glucose level as a variable for assessing individuals with established T2D.

Impact of Haemodialysis on Insulin Kinetics of Acute Kidney Injury Patients in Critical Care.

Critically ill patients are occasionally associated with an abrupt decline in renal function secondary to their primary diagnosis. The effect and impact of haemodialysis (HD) on insulin kinetics and endogenous insulin secretion in critically ill patients remains unclear. This study investigates the insulin kinetics of patients with severe acute kidney injury (AKI) who required HD treatment and glycaemic control (GC). Evidence shows that tight GC benefits the onset and progression of renal involvement in precocious phases of diabetic nephropathy for type 2 diabetes. The main objective of GC is to reduce hyperglycaemia while determining insulin sensitivity. Insulin sensitivity (SI ) is defined as the body response to the effects of insulin by lowering blood glucose levels. Particularly, this study used SI to track changes in insulin levels during HD therapy. Model-based insulin sensitivity profiles were identified for 51 critically ill patients with severe AKI on specialized relative insulin nutrition titration GC during intervals on HD (OFF/ON) and after HD (ON/OFF). The metabolic effects of HD were observed through changes in SI over the ON/OFF and OFF/ON transitions. Changes in model-based SI at the OFF/ON and ON/OFF transitions indicate changes in endogenous insulin secretion and/or changes in effective insulin clearance. Patients exhibited a median reduction of -29 % (interquartile range (IQR): [-58, 6 %], p = 0.02) in measured SI after the OFF/ON dialysis transition, and a median increase of +9 % (IQR -15 to 28 %, p = 0.7) after the ON/OFF transition. Almost 90 % of patients exhibited decreased SI at the OFF/ON transition, and 55 % exhibited increased SI at the ON/OFF transition. Results indicate that HD commencement has a significant effect on insulin pharmacokinetics at a cohort and per-patient level. These changes in metabolic behaviour are most likely caused by changes in insulin clearance or/and endogenous insulin secretion.

Analysis of Flow Through Extra-Anatomic Bypasses Between Supra-Aortic Branches Using Particle Image Velocimetry (PIV).

Supra-aortic extra-anatomic debranch (SAD) are prosthetic surgical grafts used to revascularize head and neck arteries that would be blocked during a surgical or hybrid procedure used in treating ascending and arch of the aorta pathologies. However, bypassing the supra-aortic arteries but not occluding their orifice might introduce potential for competitive flow that reduces bypass patency. Competitive flow within the bypasses across the supra-aortic arteries has not previously been identified. This research identified haemodynamics due to prophylactic inclusion of bypasses from the brachiocephalic artery (BCA) to the left common carotid artery (LCCA), and from the LCCA to left subclavian artery (LSA). Four model configurations investigated the risk of competitive flow and the necessity of intentionally blocking the proximal LSA and/or LCCA. Particle image velocimetry (PIV) was used to assess haemodynamics in each model configuration. We found potential for competitive flow in the BCA-LCCA bypass when the LSA was blocked, in the LSA-LCCA bypass, when the LCCA alone or LCCA and LSA were blocked. Flow stagnated at the start of systole within the RCCA-LCCA bypass, along with notable recirculation zones and reciprocating flow occurring throughout systolic flow. Flow also stagnated in the LCCA-LSA bypass when the LCCA was blocked. There was a large recirculation in the LCCA-LSA bypass when both the LCCA and LSA were blocked. The presence of competitive flow in all other configurations indicated that it is necessary to block or ligate the native LCCA and LSA once the debranch is made and the thoracic endovascular aortic repair (TEVAR) completed.

Improvements in glucose metabolism and insulin sensitivity with a low-carbohydrate diet in obese patients with type 2 diabetes.

OBJECTIVE: The optimal diet for weight loss in type 2 diabetes remains controversial. This study examined a low-carbohydrate, high-fat diet with detailed physiological assessments of insulin sensitivity, glycemic control, and risk factors for cardiovascular disease. METHODS: Fourteen obese patients (body mass index [BMI] 40.6 ± 4.9 kg/m(2)) with type 2 diabetes were recruited for an "Atkins"-type low-carbohydrate diet. Measurements were made at 0, 12, and 24 weeks of weight, insulin sensitivity, HbA1c, lipids, and blood pressure. RESULTS: Twelve completers lost a mean of 9.7 ± 1.8 kg over 24 weeks attributable to a major reduction in carbohydrates and resultant reduction in total energy intake. Glycemic control significantly improved (HbA1c -1.1 ± 0.25%) with reductions in hypoglycemic medication. Fasting glucose, homeostasis model assessment (HOMA), and area under the curve (AUC) glucose (intravenous glucose tolerance test [IVGTT]) were significantly reduced by week 12 ( p < 0.05). There were nonsignificant improvements in insulin sensitivity (SI) at week 12 ( p = 0.19) and week 24 ( p = 0.31). Systolic blood pressure was reduced (mean -10.0 mmHg between weeks 0 and 24, p = 0.13). Mean high-density lipoprotein (HDL), low-density lipoprotein (LDL), and total cholesterol all increased. The ratio of total: HDL cholesterol and triglycerides was reduced. CONCLUSION: A low-carbohydrate diet was well tolerated and achieved weight loss over 24 weeks in subjects with diabetes. Glycemic control improved with a reduction in requirements for hypoglycemic agents.

Comparing Literature- and Subreddit-Derived Laboratory Values in Polycystic Ovary Syndrome (PCOS): Validation of Clinical Data Posted on PCOS Reddit Forums.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a heterogeneous condition that affects 4% to 21% of people with ovaries. Inaccessibility or dissatisfaction with clinical treatment for PCOS has led to some individuals with the condition discussing their experiences in specialized web-based forums. OBJECTIVE: This study explores the feasibility of using such web-based forums for clinical research purposes by gathering and analyzing laboratory test results posted in an active PCOS forum, specifically the PCOS subreddit hosted on Reddit. METHODS: We gathered around 45,000 posts from the PCOS subreddit. A random subset of 5000 posts was manually read, and the presence of laboratory test results was labeled. These labeled posts were used to train a machine learning model to identify which of the remaining posts contained laboratory results. The laboratory results were extracted manually from the identified posts. These self-reported laboratory test results were compared with values in the published literature to assess whether the results were concordant with researcher-published values for PCOS cohorts. A total of 10 papers were chosen to represent published PCOS literature, with selection criteria including the Rotterdam diagnostic criteria for PCOS, a publication date within the last 20 years, and at least 50 participants with PCOS. RESULTS: Overall, the general trends observed in the laboratory test results from the PCOS web-based forum were consistent with clinically reported PCOS. A number of results, such as follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone, were concordant with published values for patients with PCOS. The high consistency of these results among the literature and when compared to the subreddit suggests that follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone are more consistent across PCOS phenotypes than other test results. Some results, such as testosterone, sex hormone-binding globulin, and homeostasis model assessment-estimated insulin resistance index, were between those of PCOS literature values and normal values, as defined by clinical testing limits. Interestingly, other results, including dehydroepiandrosterone sulfate, luteinizing hormone, and fasting glucose, appeared to be slightly more dysregulated than those reported in the literature. CONCLUSIONS: The differences between the forum-posted results and those published in the literature may be due to the selection process in clinical studies and the possibility that the forum disproportionally describes PCOS phenotypes that are less likely to be alleviated with medical intervention. However, the degree of concordance in most laboratory test values implied that the PCOS web-based forum participants were representative of research-identified PCOS cohorts. This validation of the PCOS subreddit grants the possibility for more research into the contents of the subreddit and the idea of undertaking similar research using the contents of other medical internet forums.

Analysing multi-perspective patient-related data during laparoscopic gynaecology procedures.

Fusing data from different medical perspectives inside the operating room (OR) sets the stage for developing intelligent context-aware systems. These systems aim to promote better awareness inside the OR by keeping every medical team well informed about the work of other teams and thus mitigate conflicts resulting from different targets. In this research, a descriptive analysis of data collected from anaesthesiology and surgery was performed to investigate the relationships between the intra-abdominal pressure (IAP) and lung mechanics for patients during laparoscopic procedures. Data of nineteen patients who underwent laparoscopic gynaecology were included. Statistical analysis of all subjects showed a strong relationship between the IAP and dynamic lung compliance (r = 0.91). Additionally, the peak airway pressure was also strongly correlated to the IAP in volume-controlled ventilated patients (r = 0.928). Statistical results obtained by this study demonstrate the importance of analysing the relationship between surgical actions and physiological responses. Moreover, these results form the basis for developing medical decision support models, e.g., automatic compensation of IAP effects on lung function.