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Health & Place Jun 2024The UN-Habitat World Cities Report 2020 highlighted that overcrowded housing, not urban density, is the major contributing factor to the spread of COVID-19. The...
The UN-Habitat World Cities Report 2020 highlighted that overcrowded housing, not urban density, is the major contributing factor to the spread of COVID-19. The relatively successful ability of densely populated cities such as Seoul, Singapore, Tokyo and New York City to manage virus spread supports this. We hypothesise that, given the complexity of the interaction between people and place, the relative contribution of density and crowding to the spread of infectious diseases may be contingent on local factors. To directly compare the role of urban density and household overcrowding, we examine each in relation to COVID-19 incidence in the three largest cities in Australia, Sydney, Melbourne and Brisbane, as the pandemic unfolded from July 2021 to January 2022. Using ecological models adjusted for spatial autocorrelation and area-level measures of age and socio-economic factors, we assess the association between population density, overcrowding in homes, and COVID-19 infections in local neighbourhoods. Challenging prevailing assumptions, we find evidence for an effect of both density and overcrowding on COVID-19 infections depending on the city and area within cities; that is, depending on the local context. For example, in the southwestern suburbs of Sydney, the case rate decreases by between 0.4 and 6.4 with every one-unit increase in gross density however the case rate increases by between 0.01 and 9.6 with every one-unit increase in total overcrowding. These findings have important implications for developing pandemic response strategies: public health measures that target either density (e.g., lockdowns and restricted range of travel) or overcrowding (e.g., restricting number of people relative to dwelling, mask-wearing indoors, vaccination prioritisation) must be cognisant of the geographically local contexts in which they are implemented.
PubMed: 38901135
DOI: 10.1016/j.healthplace.2024.103298 -
PloS One 2024Stable isotope methods have been used to study protein metabolism in humans; however, there application in dogs has not been frequently explored. The present study... (Comparative Study)
Comparative Study
Stable isotope methods have been used to study protein metabolism in humans; however, there application in dogs has not been frequently explored. The present study compared the methods of precursor (13C-Leucine), end-products (15N-Glycine), and amino acid oxidation (13C-Phenylalanine) to determine the whole-body protein turnover rate in senior dogs. Six dogs (12.7 ± 2.6 years age, 13.6 ± 0.6 kg bodyweight) received a dry food diet for maintenance and were subjected to all the above-mentioned methods in succession. To establish 13C and 15N kinetics, according to different methodologies blood plasma, urine, and expired air were collected using a specifically designed mask. The volume of CO2 was determined using respirometry. The study included four methods viz. 13C-Leucine, 13C-Phenylalanine evaluated with expired air, 13C-Phenylalanine evaluated with urine, and 15N-Glycine, with six dogs (repetitions) per method. Data was subjected to variance analysis and means were compared using the Tukey test (P<0.05). In addition, the agreement between the methods was evaluated using Pearson correlation and Bland-Altman statistics. Protein synthesis (3.39 ± 0.33 g.kg-0,75. d-1), breakdown (3.26 ± 0.18 g.kg-0.75.d-1), and flux estimations were similar among the four methods of study (P>0.05). However, only 13C-Leucine and 13C-Phenylalanine (expired air) presented an elevated Pearson correlation and concordance. This suggested that caution should be applied while comparing the results with the other methodologies.
Topics: Animals; Dogs; Oxidation-Reduction; Leucine; Phenylalanine; Carbon Isotopes; Amino Acids; Male; Nitrogen Isotopes; Glycine; Proteins; Female
PubMed: 38900837
DOI: 10.1371/journal.pone.0305073 -
Insights Into Imaging Jun 2024To evaluate the safety of a minimum continuous positive airway pressure of 4 cmHO (CPAP + 4) during computed tomography (CT)-guided radiofrequency ablation (RFA)...
OBJECTIVE
To evaluate the safety of a minimum continuous positive airway pressure of 4 cmHO (CPAP + 4) during computed tomography (CT)-guided radiofrequency ablation (RFA) for lung malignancies under procedural sedation and analgesia (PSA).
METHODS
This was a prospective, randomised, single-blind, parallel-group, placebo-controlled trial with an open-label medical device conducted at a single tertiary university hospital in Barcelona, Spain. Forty-six patients over 18 years of age scheduled for CT-guided RFA of a malignant pulmonary tumour under PSA were randomised to receive either CPAP + 4 or a modified mask for placebo CPAP (Sham-CPAP). Exclusion criteria included contraindications for RFA, refusal to participate, inability to understand the procedure or tolerate the CPAP test, lung biopsy just prior to RFA, intercurrent diseases, or previous randomisation for additional pulmonary RFA. Primary outcomes were the percentage of patients reporting at least one serious adverse event (SAE), classification for complications from the Cardiovascular and Interventional Radiological Society of Europe (CIRSE), and Clavien-Dindo classifications for complications, hospital stay, and readmissions. Secondary outcomes included adverse events (AEs), respiratory parameters, airway management, and the local radiological efficacy of pulmonary ablation.
RESULTS
CPAP + 4 prolonged hospital stay (1.5 ± 1.1 vs. 1.0 ± 0 inpatient nights, p = 0.022) and increased the risk of AE post-RFA (odds ratio (95% CI): 4.250 (1.234 to 14.637), p = 0.021 with more pneumothorax cases (n = 5/22, 22.7% vs. n = 0/24, 0%, p = 0.019). Per-protocol analysis revealed more SAEs and CIRSE grade 3 complications in the CPAP + 4 group (23.5% vs. 0%, p = 0.036). No significant differences were found in the effectiveness of oxygenation, ventilation, or pulmonary ablation.
CONCLUSION
CPAP is unsafe during CT-guided RFA for lung cancer under PSA even at the lowest pressure setting.
TRIAL REGISTRATION
ClinicalTrials.Gov, ClinicalTrials.gov ID NCT02117908, Registered 11 April 2014, https://www.
CLINICALTRIALS
gov/study/NCT02117908 CRITICAL RELEVANCE STATEMENT: This study highlights the hazards of continuous positive airway pressure during radiofrequency ablation of lung cancer, even at minimal pressures, deeming it unsafe under procedural sedation and analgesia in pulmonary interventional procedures. Findings provide crucial insights to prioritise patient safety.
KEY POINTS
No prior randomised controlled trials on CPAP safety in percutaneous lung thermo-ablation. Standardised outcome measures are crucial for radiology research. CPAP during lung RFA raises hospital stay and the risk of complications. CPAP is unsafe during CT-guided RFA of lung cancer under procedural sedoanalgesia.
PubMed: 38900225
DOI: 10.1186/s13244-024-01721-9 -
Journal of Otolaryngology - Head & Neck... 2024Adenotonsillectomy is one of the most common surgical procedures worldwide. The current standard for securing the airway in patients undergoing adenotonsillectomy is... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Adenotonsillectomy is one of the most common surgical procedures worldwide. The current standard for securing the airway in patients undergoing adenotonsillectomy is endotracheal tube (ETT) intubation. Several studies have investigated the use of the laryngeal mask airway (LMA) in this procedure. We conducted a systematic review and meta-analysis to compare the safety and efficacy of the LMA versus ETT in adenotonsillectomy.
METHOD
Databases were searched from inception to 2022 for randomized controlled trials and comparative studies. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The primary outcome is the rate of perioperative respiratory adverse events (PRAEs). Secondary outcomes included the rate of conversion to ETT, desaturations, nausea/vomiting, and surgical time. A subgroup analysis, risk of bias, publication bias, and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) assessments were also performed.
RESULTS
Twelve studies were included in the analysis (4176 patients). The mean overall conversion to ETT was 8.36% [95% confidence interval (CI) = 8.17, 8.54], and for the pediatric group 8.27% (95% CI = 8.08, 8.47). The mean rate of conversion to ETT secondary to complications was 2.89% (95% CI = 2.76, 3.03) while the rest was from poor surgical access. Overall, there was no significant difference in PRAEs [odds ratio (OR) 1.16, 95% CI = 0.60, 2.22], desaturations (OR 0.79, 95% CI = 0.38, 1.64), or minor complications (OR 0.89, 95% CI = 0.50, 1.55). The use of LMA yielded significantly shorter operative time (mean difference -4.38 minutes, 95% CI = -8.28, -0.49) and emergence time (mean difference -4.15 minutes, 95% CI = -5.63, -2.67).
CONCLUSION
For adenotonsillectomy surgery, LMA is a safe alternative to ETT and requires less operative time. Careful patient selection and judgment of the surgeon and anesthesiologist are necessary, especially given the 8% conversion to ETT rate.
Topics: Humans; Tonsillectomy; Adenoidectomy; Laryngeal Masks; Intubation, Intratracheal; Postoperative Complications
PubMed: 38899617
DOI: 10.1177/19160216241263851 -
IEEE Open Journal of Engineering in... 2024Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds...
Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods. We propose a masked-based architecture similar to Conv-TasNet. The encoder and decoder consist of 1D convolution and 1D transposed convolution, while the mask generator consists of a convolution and transformer architecture. The input chest sounds were first encoded as a sequence of tokens using 1D convolution. The tokens were then passed to the mask generator to generate two masks, one for heart sounds and one for lung sounds. Each mask is then applied to the input token sequence. Lastly, the tokens are converted back to waveforms using 1D transposed convolution. Our proposed model showed superior results compared to the previous methods based on objective distortion measures, ranging from a 2.01 dB improvement to a 5.06 dB improvement. The proposed model is also significantly faster than the previous methods, with at least a 17-time improvement. The proposed model could be a suitable preprocessing step for any health monitoring system where only the heart sound or lung sound is desired.
PubMed: 38899018
DOI: 10.1109/OJEMB.2024.3401571 -
Human Brain Mapping Jun 2024Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS)...
Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS) is related to increased tissue loss. The use of mcDESPOT (multicomponent driven single pulse observation of T1 and T2), a 3D imaging method which exploits both the T1 and T2 contrasts, allows FWF to be derived in clinically feasible times. However, this method has not been used to quantify changes of FWF and their potential clinical impact in MS. The aim of this study is to investigate the changes in FWF in MS patients and their relationship with tissue damage and cognition, under the hypothesis that FWF is a proxy of clinically meaningful tissue loss. To this aim, we tested the relationship between FWF, MS lesion burden and information processing speed, evaluated via the Symbol Digit Modalities Test (SDMT). In addition to standard sequences, used for T1- and T2-weighted lesion delineation, the mcDESPOT sequence with 1.7 mm isotropic resolution and a diffusion weighted imaging protocol (b = 0, 1200 s/mm, 40 diffusion directions) were employed at 3 T. The fractional anisotropy map derived from diffusion data was used to define a subject-specific white matter (WM) atlas. Brain parenchyma segmentation returned masks of gray matter (GM) and WM, and normal-appearing WM (NAWM), in addition to the T1 and T2 lesion masks (T1L and T2L, respectively). Ninety-nine relapsing-remitting MS patients (age = 43.3 ± 9.9 years, disease duration 12.3 ± 7.7 years) were studied, together with twenty-five healthy controls (HC, age = 38.8 ± 11.0 years). FWF was higher in GM and NAWM of MS patients, compared to GM and WM of HC (both p < .001). In MS patients, FWF was the highest in the T1L and GM, followed by T2L and NAWM, respectively. FWF increased significantly with T1L and T2L volume (ρ ranging from 0.40 to 0.58, p < .001). FWF in T2L was strongly related to both T1L volume and the volume ratio T1L/T2L (ρ = 0.73, p < .001). MS patients performed worse than HC in the processing speed test (mean ± SD: 54.1 ± 10.3 for MS, 63.8 ± 10.8 for HC). FWF in GM, T2L, perilesional tissue and NAWM increased with SDMT score reduction (ρ = -0.30, -0.29, -0.33 respectively and r = -.30 for T2L, all with p < .005). A regional analysis, conducted to determine which NAWM regions were of particular importance to explain the relationship between FWF and cognitive impairment, revealed that FWF spatial variance was negatively related to SDMT score in the corpus callosum and the superior longitudinal fasciculus, WM structures known to be associated with cognitive impairment, in addition to the left corticospinal tract, the sagittal stratum, the right anterior limb of internal capsule. In conclusion, we found excess free water in brain parenchyma of MS patients, an alteration that involved not only MS lesions, but also the GM and NAWM, impinging on brain function and negatively associated with cognitive processing speed. We suggest that the FWF metric, derived from noninvasive, rapid MRI acquisitions and bearing good biological interpretability, may prove valuable as an MRI biomarker of tissue damage and associated cognitive impairment in MS.
Topics: Humans; Female; Male; Adult; Middle Aged; Brain; Multiple Sclerosis; Diffusion Magnetic Resonance Imaging; Water; Cognitive Dysfunction; Parenchymal Tissue; White Matter; Gray Matter; Processing Speed
PubMed: 38895882
DOI: 10.1002/hbm.26761 -
Sensors (Basel, Switzerland) Jun 2024The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products...
The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.
PubMed: 38894426
DOI: 10.3390/s24113635 -
Sensors (Basel, Switzerland) Jun 2024Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it...
Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.
PubMed: 38894374
DOI: 10.3390/s24113578 -
Sensors (Basel, Switzerland) May 2024Considering the complex structure of Chinese characters, particularly the connections and intersections between strokes, there are challenges in low accuracy of Chinese...
Considering the complex structure of Chinese characters, particularly the connections and intersections between strokes, there are challenges in low accuracy of Chinese character stroke extraction and recognition, as well as unclear segmentation. This study builds upon the YOLOv8n-seg model to propose the YOLOv8n-seg-CAA-BiFPN Chinese character stroke fine segmentation model. The proposed Coordinate-Aware Attention mechanism (CAA) divides the backbone network input feature map into four parts, applying different weights for horizontal, vertical, and channel attention to compute and fuse key information, thus capturing the contextual regularity of closely arranged stroke positions. The network's neck integrates an enhanced weighted bi-directional feature pyramid network (BiFPN), enhancing the fusion effect for features of strokes of various sizes. The Shape-IoU loss function is adopted in place of the traditional CIoU loss function, focusing on the shape and scale of stroke bounding boxes to optimize the bounding box regression process. Finally, the Grad-CAM++ technique is used to generate heatmaps of segmentation predictions, facilitating the visualization of effective features and a deeper understanding of the model's focus areas. Trained and tested on the public Chinese character stroke datasets CCSE-Kai and CCSE-HW, the model achieves an average accuracy of 84.71%, an average recall rate of 83.65%, and a mean average precision of 80.11%. Compared to the original YOLOv8n-seg and existing mainstream segmentation models like SegFormer, BiSeNetV2, and Mask R-CNN, the average accuracy improved by 3.50%, 4.35%, 10.56%, and 22.05%, respectively; the average recall rates improved by 4.42%, 9.32%, 15.64%, and 24.92%, respectively; and the mean average precision improved by 3.11%, 4.15%, 8.02%, and 19.33%, respectively. The results demonstrate that the YOLOv8n-seg-CAA-BiFPN network can accurately achieve Chinese character stroke segmentation.
PubMed: 38894271
DOI: 10.3390/s24113480 -
Sensors (Basel, Switzerland) May 2024In order to improve the efficiency and accuracy of multitarget detection of soldering defects on surface-mounted components in Printed Circuit Board (PCB) fabrication,...
In order to improve the efficiency and accuracy of multitarget detection of soldering defects on surface-mounted components in Printed Circuit Board (PCB) fabrication, we propose a sample generation method using Stable Diffusion Model and ControlNet, as well as a defect detection method based on the Swin Transformer. The method consists of two stages: First, high-definition original images collected in industrial production and the corresponding prompts are input to Stable Diffusion Model and ControlNet for automatic generation of nonindependent samples. Subsequently, we integrate Swin Transformer as the backbone into the Cascade Mask R-CNN to improve the quality of defect features extracted from the samples for accurate detection box localization and segmentation. Instead of segmenting individual components on the PCB, the method inspects all components in the field of view simultaneously over a larger area. The experimental results demonstrate the effectiveness of our method in scaling up nonindependent sample datasets, thereby enabling the generation of high-quality datasets. The method accurately recognizes targets and detects defect types when performing multitarget inspection on printed circuit boards. The analysis against other models shows that our improved defect detection and segmentation method improves the Average Recall (AR) by 2.8% and the mean Average Precision (mAP) by 1.9%.
PubMed: 38894263
DOI: 10.3390/s24113473