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Fulltext Fine needle aspiration of lung lesion: Lateral and transpectoral approach

Smith K

Biomed Imaging Interv J, 2012 Jan;8(1):e2.
PMID: 22970058 DOI: 10.2349/biij.8.1.e2

A 69 year-old man presented with an incidental finding on radiograph of a lesion in the left upper lobe. CT indicated it was likely to be a neoplasm and CT-guided FNA was requested. The lesion was located medial to the scapula so a creative approach was utilised to gain access to the lesion. This study discusses the approach used and why it reduced patient risk compared to a more conventional procedure. The sample was positive for neoplasm and there were no complications arising from the procedure.
Fulltext An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals

Khare SK, Acharya UR

Comput Biol Med, 2023 Mar;155:106676.
PMID: 36827785 DOI: 10.1016/j.compbiomed.2023.106676

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and timely medication can help individuals with ADHD perform daily tasks without difficulty. Electroencephalogram (EEG) signals can help neurologists to detect ADHD by examining the changes occurring in it. The EEG signals are complex, non-linear, and non-stationary. It is difficult to find the subtle differences between ADHD and healthy control EEG signals visually. Also, making decisions from existing machine learning (ML) models do not guarantee similar performance (unreliable).
METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children.
RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively.
CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.
SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals

Khare SK, Bajaj V, Acharya UR

Physiol Meas, 2023 Mar 08;44(3).
PMID: 36787641 DOI: 10.1088/1361-6579/acbc06

Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.
Chronic melioidosis

Smith KV, Grimmond T, Monk I

Med J Aust, 1975 Sep 20;2(12):479-80.
PMID: 1196186

This report is of a man who suffered from chronic melioidosis contracted in Malaysia. In the course of the disease he had a lobe of a lung resected, developed empyema and, while this was still draining, developed infection in an ankle. Both the empyema thoracis and the ankle infection were due to Pseudomonas pseudomallel. He now appears to be cured, probably by massive doses of tetracycline.
Dermal myiasis in man caused by Sarcophaga krameri (Boettcher) (Diptera: Sarcophagidae) in Malaysia

Thomas V, Smith KG, Dear JP

Southeast Asian J Trop Med Public Health, 1980 Mar;11(1):140.
PMID: 7403944
Mechanisms of maintenance of tropical freshwater fish communities in the face of disturbance

Martin-Smith KM, Laird LM, Bullough L, Lewis MG

Philos Trans R Soc Lond B Biol Sci, 1999 Nov 29;354(1391):1803-10.
PMID: 11605623

Community resistance to, and resilience from, perturbation will determine the trajectory of recovery from disturbance. Although selective timber extraction is considered a severe disturbance, fish communities from headwater streams around Danum Valley Field Centre, Sabah, Malaysia, showed few long-term changes in species composition or abundance. However, some species showed short-term (< 18 months) absence or decrease in abundance. These observations suggested that both resistance and resilience were important in maintaining long-term fish community structure. Resistance to perturbation was tested by monitoring fish communities before and after the creation of log-debris dams, while resilience was investigated by following the time-course of recolonization following complete removal of all fish. High community resistance was generally shown although the response was site-specific, dependent on the composition of the starting community, the size of the stream and physical habitat changes. High resilience was demonstrated in all recolonization experiments with strong correlations between pre- and post-defaunation communities, although there was a significant difference between pool and riffle habitats in the time-course of recovery. These differences can be explained by the movement characteristics of the species found in the different habitats. Resilience appeared to be a more predictable characteristic of the community than resistance and the implications of this for ensuring the long-term persistence of fish in the area are discussed.
Fulltext Developing a digital intervention for cancer survivors: an evidence-, theory- and person-based approach

Bradbury K, Steele M, Corbett T, Geraghty AWA, Krusche A, Heber E, et al.

NPJ Digit Med, 2019;2:85.
PMID: 31508496 DOI: 10.1038/s41746-019-0163-4

This paper illustrates a rigorous approach to developing digital interventions using an evidence-, theory- and person-based approach. Intervention planning included a rapid scoping review that identified cancer survivors' needs, including barriers and facilitators to intervention success. Review evidence (N = 49 papers) informed the intervention's Guiding Principles, theory-based behavioural analysis and logic model. The intervention was optimised based on feedback on a prototype intervention through interviews (N = 96) with cancer survivors and focus groups with NHS staff and cancer charity workers (N = 31). Interviews with cancer survivors highlighted barriers to engagement, such as concerns about physical activity worsening fatigue. Focus groups highlighted concerns about support appointment length and how to support distressed participants. Feedback informed intervention modifications, to maximise acceptability, feasibility and likelihood of behaviour change. Our systematic method for understanding user views enabled us to anticipate and address important barriers to engagement. This methodology may be useful to others developing digital interventions.