METHODS: Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies.
RESULTS: The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively.
CONCLUSIONS: This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.
METHODOLOGY: A cross-sectional study in women (n = 369, age: 46 ± 13 years, body mass index (BMI): 26.31 ± 2.54 kg/m2) was conducted. Blood samples were collected and high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, total cholesterol (TC), and triglycerides (TGs) were estimated. Subsequently, nontraditional lipid parameters were calculated, namely non-HDL-C, Castelli's Risk Index II (CRI-II), CRI-I, lipoprotein combined index (LCI), atherogenic index (AI), and AI of plasma (AIP).
RESULTS: Based on TC (≥200 mg/dL), the derived thresholds for non-HDL-C, CRI-II, CRI-I, LCI, AI, and AIP were 139 mg/dL, 2.29, 3.689, 58,066, 2.687, and 0.487, respectively. Similarly, based on the threshold of TG (≥150 mg/dL), the derived thresholds for non-HDL-C, CRI-II, CRI-I, LCI, AI, and AIP were 127 mg/dL, 2.3, 3.959, 58,251, 2.959, and 0.467, respectively. Out of considered five risk factors, non-HDL-C, CRI-II, CRI-I, LCI, and AI thresholds were capable in identifying four risk factors (physical activity, blood pressure, BMI, and age) and AIP was able to associate with two risk factors at most (blood pressure and BMI).
CONCLUSION: The derived thresholds of nontraditional lipid parameters were capable of differentiating between CVD risk and nonrisk groups suggesting the possible use of these thresholds for studying CVD risk.
AIM: To document self-reported hepatitis B vaccination history and serology results.
SETTING: A select group of high-risk HCWs in a tertiary care hospital in Banjul, the Gambia.
METHODS: This was a cross-sectional pilot study conducted from 12 June 2023 to 16 June 2023. Participants were HCWs at high risk for blood exposure who completed a health history interview prior to serology testing for hepatitis B surface antigen (HBsAg) and hepatitis B surface antibody (anti-HBs) and vaccination.
RESULTS: The pilot study enrolled 70 HCWs who were primarily female (n = 44; 62.9%). The majority of the participants, 43 (61.4%) reported having received at least one dose of the hepatitis B vaccine in the past. The overall prevalence of HBsAg positivity in this study was 4.3% (95% confidence interval [CI]: 1.5-11.9), all in older participants. Importantly, 60.0% (95% CI: 48.3-70.7) of participants had no anti-HBs detected.
CONCLUSION: This pilot study documents a higher prevalence of hepatitis B infection among older workers and the lack of anti-HBs across the majority of participants. This suggests a serious vulnerability for the individual health worker and indicates the need for a wider screening and vaccination campaign to assess the risk across the Gambian health workforce.
CONTRIBUTION: This pilot study provides the first evidence to support a wider assessment of hepatitis B serology status of Gambian health workers to gauge the need for a broader vaccine campaign.
METHODS: Randomised, double-blinded, placebo-controlled, single-centre clinical trial involving 32 patients with idiopathic small FTMHs (<400 μm $$ \upmu \mathrm{m} $$ ). Participants in both arms used topical dorzolamide 2% or saline thrice daily for 8 weeks with monthly OCT. Those with persisting FTMH underwent vitrectomy with ILM peel and gas tamponade. The primary outcome was the rate of FTMH closure at the end of treatment.
RESULTS: Between 6 March 2020 and 16 June 2023, 32 eligible patients were enrolled: 16 participants in each arm. All participants in both groups were included in the final analysis. At the final visit, 3 of 16 (18.8%) patients in both the topical dorzolamide and placebo group demonstrated closure. There was no statistically significant difference in the proportion of FTMH closure between the control and treatment group (p = 1.00), nor statistically significant difference in the mean change in best corrected visual acuity (BCVA; p = 0.909). There was no difference in the change in FTMH diameter between groups (p = 0.225). No serious adverse events were reported in either group.
CONCLUSION: Topical dorzolamide was safe but not superior to placebo in the functional and anatomical outcomes of FTMH.
PURPOSE: The purpose of this in vitro study was to incorporate microparticles into a commercially available 3D printed denture base resin and compare its mechanical and biological properties with the conventional polymethyl methacrylate (PMMA) denture base material.
MATERIAL AND METHODS: Microparticles were collected from milled zirconia blanks and were blended with a 3D printing denture base resin (NextDent Denture 3D+). The optimal zirconia microparticle content (2%) for blending and printed was determined by using a liquid-crystal display (LCD) 3D printer. The printed specimens were then postrinsed and postpolymerized based on the manufacturer's instructions. Mechanical and biological characterization were carried out in terms of flexural strength, fracture toughness, and fungal adhesion. One-way ANOVA was carried out to analyze the results statistically.
RESULTS: The incorporation of microparticles in the 3D printed denture demonstrated higher mechanical strength (104.77 ±7.60 MPa) compared with conventional heat-polymerized denture base resin (75.15 ±24.41 MPa) (P