Watchful waiting to surgery in men with mildly symptomatic or asymptomatic inguinal hernia: an individual participant data meta-analysis of long-term follow-up of randomized controlled trials.

BACKGROUND: Individual studies on men with mildly symptomatic or asymptomatic inguinal hernia who have opted for watchful waiting (WW) vary considerably. Furthermore, long-term data on such patients who cross over to herniorrhaphy is scarce. METHODS: PubMed, EMBASE, and Cochrane databases were searched systematically from inception to 3rd April 2024 for long-term follow-up of randomized controlled trials (RCTs) on men with mildly symptomatic or asymptomatic inguinal hernia. Individual participant survival data of cross over rates from WW to herniorrhaphy were extracted, reconstructed and combined. Secondary outcome was reason for cross over to herniorrhaphy. RESULTS: Long-term follow-up of three RCTs with 592 participants was included. A total of 344/592 participants crossed over to herniorrhaphy during a median follow up period that ranged from 3.2 to 12.0 years. The median cumulative cross over rate was 54.2% (95% CI 45.5% - 66.3%). The cumulative 1-year, 5-year, and 10- year cross over rates were 28.7% (95% CI 25.2% - 32.5%), 51.5% (95% CI 47.4% - 55.6%), and 70.6% (95% CI 66.2% - 74.9%) respectively. During follow-up, the most frequent reasons for cross over to herniorrhaphy were increased pain 198/344 (57.6%) and incarceration 15/344 (4.4%). CONCLUSION: This study provides valuable long-term data for patient counselling, indicating that while WW is a safe strategy for men with mildly symptomatic or asymptomatic inguinal hernia, symptoms would likely progress eventually, necessitating operative repair.

Surgical Management of Pericaecal Hernia in a Virgin Abdomen.

Internal hernia is an uncommon cause of mechanical small bowel obstruction. This case report details a 66-year-old Chinese male with no prior abdominal surgeries who presented with colicky abdominal pain, abdominal distension, and vomiting. Initial investigations were unyielding, but escalating symptoms prompted a diagnostic laparoscopy. Laparotomy then revealed a closed-loop obstruction through a lateral type pericecal hernia, with a segment of ischemic jejunum. Adhesion bands in the right iliac fossa and a congenital hernia orifice in the mesentery were identified and addressed. The patient recovered well postoperatively. This discussion explores the Meyer's classification of pericecal hernias, potential etiologies, clinical manifestations, diagnostic considerations, and the choice between laparoscopic and open surgeries. This case underscores the importance of a high index of suspicion, prompt surgical intervention, and the diagnostic utility of laparoscopy in managing pericecal hernias.

Functional comparison of phototropin from the liverworts Apopellia endiviifolia and Marchantia polymorpha.

Phototropin (phot) is a blue light (BL) receptor and thermosensor that mediates chloroplast movements in plants. Liverworts, as early-diverging plant species, have a single copy of PHOT gene, and the phot protein in each liverwort activates the signaling pathway adapted to its specific growing environment. In this study, we functionally compared phot from two different liverworts species: Apopellia endiviifolia (Aephot) and Marchantia polymorpha (Mpphot). The BL-dependent photochemical activity of Aephot was similar to that of Mpphot, whereas the thermochemical activity of Aephot was lower than that of Mpphot. Therefore, the phot-mediated signaling pathways of the two plant species may differ more in response to temperature than to BL. Furthermore, we analyzed the functional compatibility of Aephot and Mpphot in chloroplast movements by transiently expressing AePHOT or MpPHOT. The transient expression of AePHOT did not mediate chloroplast movement in M. polymorpha, showing the incompatibility of Aephot with the signaling pathway of M. polymorpha. By contrast, the transient expression of MpPHOT mediated chloroplast movement in A. endiviifolia, indicating the compatibility of Mpphot with the signaling pathway of A. endiviifolia. Our findings reveal both functional similarities and differences between Aephot and Mpphot proteins from the closely related liverworts.

Dark-induced chloroplast relocation depends on actin filaments in the liverwort Apopellia endiviifolia along with the light- and cold-induced relocations.

Chloroplasts move to the periclinal walls of cells under weak light to harness light energy for photosynthesis and to anticlinal walls to avoid strong light. These responses involve the cytoskeleton components microtubules and/or actin filaments. In the dark, chloroplasts move to the anticlinal cell walls bordering neighbouring cells (dark-positioning response), but this response in various plants normally requires a prolonged dark incubation period, which has hampered analysis. However, we recently demonstrated the dark-positioning response that can be induced after a short period of dark incubation in the liverwort Apopellia endiviifolia. Here, we investigated whether the cytoskeleton components function in the dark-positioning response of A. endiviifolia cells. Microtubules and actin filaments were fluorescently visualised in A. endiviifolia cells and were disrupted following treatment with the microtubule and actin filament polymerisation inhibitors. The dark-positioning response was unaffected in the cells with disrupted microtubules. By contrast, the dark-positioning response was inhibited by the disruption of actin filaments. The disruption of actin filaments also restricted chloroplast mobility during light- and cold-dependent chloroplast movements in A. endiviifolia. Therefore, the dark-positioning response of A. endiviifolia depends solely on an actin filament-associated motility mechanism, as do the light- and cold-dependent chloroplast responses.

Predicting the Severity of Adverse Events on Osteoporosis Drugs Using Attribute Weighted Logistic Regression.

Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients' lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.

Incremental Learning of Human Activities in Smart Homes.

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques.

Suicide is a major public-health problem that exists in virtually every part of the world. Hundreds of thousands of people commit suicide every year. The early detection of suicidal ideation is critical for suicide prevention. However, there are challenges associated with conventional suicide-risk screening methods. At the same time, individuals contemplating suicide are increasingly turning to social media and online forums, such as Reddit, to express their feelings and share their struggles with suicidal thoughts. This prompted research that applies machine learning and natural language processing techniques to detect suicidality among social media and forum users. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. To achieve this objective, we conducted a literature review of the recent articles detailing machine learning and natural language processing techniques applied to Reddit data to detect the presence of suicidal ideations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we selected 26 recent studies, published between 2018 and 2022. The findings of the review outline the prevalent methods of data collection, data annotation, data preprocessing, feature engineering, model development, and evaluation. Furthermore, we present several Reddit-based datasets utilized to construct suicidal ideation detection models. Finally, we conclude by discussing the current limitations and future directions in the research of suicidal ideation detection.

A pilot randomized controlled trial of WeiNaoKang (SaiLuoTong) in treating vascular dementia.

Objective: WeiNaoKang (or SaiLuoTong) is an herbal formula consisting of ginkgo, ginseng, and saffron. Our objective was to investigate if WeiNaoKang could improve cognitive function and cerebral perfusion in patients suffering from vascular dementia. Methods: A 16-week randomized double-blind, placebo-controlled trial was carried out in the setting of a memory disorder clinic at a single center. Patients with vascular dementia diagnosed clinically but supported by imaging and other investigations were invited to participate. The diagnoses were based on the National Institute of Neurological Disorders and Stroke/Association Internationale pour la Recherche et l'Enseignement en Neurosciences (NINDS-AIREN) criteria. An independent blinded assessor evaluated the effects of the formula. Intervention group was compared to the control group. A subgroup of participants was randomly chosen for further evaluation of cerebral perfusion by single photon emission computed tomography scans post-treatment. Results: Both groups were comparable in age (mean = 74 ± 7.2 years in the placebo group and 75 ± 7.4 in the intervention group) and in other demographics. Sixty-two participants were included in final analysis. Alzheimer's Disease Assessment Scale - Cognitive Portion (ADAS-cog) was the primary outcome. By week 16, the mean ADAS-cog reduced from 24.48 to 20.30 (mean reduction = 4.18) for those in the treatment group, and from 18.98 to 17.81 (mean reduction = 1.18) for those in the placebo group. The difference in mean reduction of ADAS-cog was -3.00 (95% confidence interval [CI] = -4.910 to -1.100) in favor of the treatment group. Secondary outcomes of activities of daily living and quality of life measures also showed significant difference. In the perfusion scan analysis, the difference in the change in cerebral blood flow (t-scores) pre- and post-treatment between the intervention group (n = 7) and the placebo group (n = 11) was statistically significant (P < 0.001). Conclusion: In this randomized, double-blind placebo-controlled trial, we demonstrated significant differences in improvement in cognitive function and activities of daily living. The clinical improvement is corroborated with improvement in cerebral perfusion in a subset of participants.

Chloroplast relocation movement in the liverwort Apopellia endiviifolia.

Changes in the subcellular localisation of chloroplasts help optimise photosynthetic activity under different environmental conditions. In many plants, this movement is mediated by the blue-light photoreceptor phototropin. A model organism with simple phototropin signalling that allows clear observation of chloroplasts would facilitate the study of chloroplast relocation movement. Here, we examined this process in the simple thalloid liverwort Apopellia endiviifolia. Transverse sections of the thallus tissue showed uniformly developed chloroplasts and no air chambers; these characteristics enable clear observation of chloroplasts and analysis of their movements under a fluorescence stereomicroscope. At 22°C, the chloroplasts moved to the anticlinal walls of cells next to the neighbouring cells in the dark (dark-positioning response), whereas they moved towards weak light (accumulation response) and away from strong light (avoidance response). When the temperature was reduced to 5°C, the chloroplasts moved away from weak light (cold-avoidance response). Hence, both light- and temperature-dependent chloroplast relocation movements occur in A. endiviifolia. Notably, the accumulation, avoidance and cold-avoidance responses were induced under blue-light but not under red-light. These results suggest that phototropin is responsible for chloroplast relocation movement in A. endiviifolia and that the characteristics are similar to those in the model liverwort Marchantia polymorpha. RNA sequencing and Southern blot analysis identified a single copy of the PHOTOTROPIN gene in A. endiviifolia, indicating that a simple phototropin signalling pathway functions in A. endiviifolia. We conclude that A. endiviifolia has great potential as a model system for elucidating the mechanisms of chloroplast relocation movement.

Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes.

Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant's activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.

Designs to balance cost and success rate for an early phase clinical study.

We present an initial exploration of a fully cost-driven design. A design criterion was proposed that represented the minimum expected cost of an early phase clinical study, where costs include resource use as well as study failure. The design was based on attainment of a target concentration in a cohort of study participants. The model and parameter values arose from a previous population pharmacokinetic analysis of a phase I study. The resulting design naturally balanced the cost and the success rate of an early phase clinical study, without the need to define arbitrary constraints on the design space.

Population pharmacokinetics of intramuscular droperidol in acutely agitated patients.

BACKGROUND: Intramuscular droperidol is used increasingly for sedation of aggressive and violent patients. This study aimed to characterise the pharmacokinetics of intramuscular droperidol in these patients to determine how rapidly it is absorbed and the expected duration of measurable drug concentrations. METHODS: We undertook a population pharmacokinetic analysis of a subgroup of patients from a clinical trial comparing droperidol and midazolam: 17 receiving 5 mg and 24 receiving 10 mg droperidol. Droperidol was measured using high-performance liquid chromatography. Pharmacokinetic modelling was performed under a nonlinear mixed effects modelling framework (NONMEM v7.2). The model was used to simulate concentration time profiles of three typical doses, 5 mg, 10 mg and 10 mg + 10 mg repeated at 15 min. RESULTS: A two-compartment first-order input with first-order output model fitted the data best. The absorption rate constant was poorly characterised by the data and an estimate of the first order rate constant of absorption when fixed to 10 h-1 provided a stable model and lowest objective function. This represents extremely rapid absorption with a half-life of 5 min. The final model had a clearance of 41.9 l h-1 and volume of distribution of the central compartment of, 73.6 l. Median and interquartile range of initial (alpha) half-life was 0.32 h (0.26-0.37 h) and second (beta) half-life was 3.0 h (2.5-3.6 h). Simulations indicate that 10 mg alone provides an 80% probability of being above the lower limit of quantification (5 µg l-1 ) for 7 h, 2 h longer than for 5 mg. Giving two 10 mg doses increased this duration to 10 h. CONCLUSIONS: Intramuscular droperidol is rapidly absorbed with high therapeutic concentrations after 5 and 10 mg doses, and supports clinical data in which droperidol sedates rapidly for up to 6 h.

Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches.

BACKGROUND: Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs. RESULTS: At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community. CONCLUSIONS: This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

Automated identification of copepods using digital image processing and artificial neural network.

BACKGROUND: Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. RESULTS: We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus). CONCLUSIONS: The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images.

Comparison of robust criteria for D-optimal designs.

This study compared the performance of a local and three robust optimality criteria in terms of the standard error for a one-parameter and a two-parameter nonlinear model with uncertainty in the parameter values. The designs were also compared in conditions where there was misspecification in the prior parameter distribution. The impact of different correlation between parameters on the optimal design was examined in the two-parameter model. The designs and standard errors were solved analytically whenever possible and numerically otherwise.

Health literacy in adult education: a natural partnership for health equity.

Incorporating health literacy in adult education instruction is a promising approach to increasing the health equity of people who face racial/ethnic health disparities. Six adult education centers throughout a small Northeast state received 1-year Health Literacy Project grants from a local foundation to increase their capacity to teach health literacy through Study Circles. The evaluation of the project assessed changes in adult learners' skills needed to navigate health systems, manage chronic diseases, and engage in preventive behavior; learners' self-efficacy; and how the education centers increased their capacity to teach health literacy skills to adult learners of color. Quantitative and qualitative data indicated that students' knowledge about health issues and self-efficacy increased significantly as a result of the health literacy instruction. All six centers improved their capacity to teach health literacy. By the end of the Health Literacy Project, almost three quarters of classes included health literacy instruction. Almost half of the Study Circle teachers continued to attend professional development activities for health literacy and share their knowledge with other teachers. Each center also developed partnerships with health care providers and created an infrastructure to continue to teach health literacy. Implications of the identified strengths and challenges on future efforts to increase health literacy and equity are considered.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies.

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Adaptive optimal design for bridging studies with an application to population pharmacokinetic studies.

PURPOSE: To develop and evaluate methods for conducting adaptive population pharmacokinetic bridging studies. METHODS: An adaptive D-optimal design based on optimization of the population Fisher information matrix was used to determine the best sampling schedule for a target-population. Recruitment of the target-population was divided into batches and patients are assumed to enroll by batch. A prior-population model was used to determine the optimal sampling schedule for the first batch and to stabilise the data analysis in the interim iteration. Simulation studies were performed under two scenarios (1) the prior- and target-populations have similar pharmacokinetic profiles and (2) the pharmacokinetic profiles diverge significantly. A design criterion to determine early full enrollment was also proposed. RESULTS: The target-population estimates obtained using the proposed method were compared to estimates obtained if the target-population was studied with a design optimized based on the prior-population model. The proposed method is shown to be not inferior in scenario (1) and superior in scenario (2). The criterion to determine early full enrollment was proven to be effective. CONCLUSIONS: An adaptive optimal design method together with an early full enrollment criterion were evaluated and resulted in more accurate estimates for the target-population in bridging studies.

Phaeomycotic cysts caused by Phoma species.

Phoma species are primarily phytopathogens which have been reported to sporadically cause human disease. We report a patient with phaeohyphomycotic cysts caused by Phoma species, which were initially mistaken for ganglions.

Methods of robust design of nonlinear models with an application to pharmacokinetics.

Optimal design methods for nonlinear models are dependent on the true but unknown parameter values. Criteria for developing designs that are robust to the choice of parameter values such as ED optimality have been proposed. However, these criteria are computationally intensive and can perform poorly at extremes of the prior parameter distribution. Two different criteria are proposed. Both involve evaluation of the determinant of the Fisher information matrix over models formed at various combinations of the 2.5th and 97.5th percentiles of the parameter space. The performance of the proposed optimality criteria is compared to two existing robust optimal design criteria.