Correction: From theory to practice: An integrated TTF-UTAUT study on electric vehicle adoption behavior.

[This corrects the article DOI: 10.1371/journal.pone.0297890.].

Handgrip strength and mortality in a cohort of kidney failure patients: Comparative analysis of different normalization methods.

OBJECTIVES: Reduced handgrip strength (HGS) is associated with adverse clinical outcomes. We analyzed and compared associations of HGS with mortality risk in dialysis patients, using different normalization methods of HGS. METHODS: HGS and clinical and laboratory parameters were measured in a cohort of 446 incident dialysis patients (median age 56 y, 62% men). The area under the receiver operating characteristic curve (AUROC) was used to compare different normalization methods of HGS as predictors of mortality: absolute HGS in kilograms; HGS normalized to height, weight, or body mass index; and HGS of a reference population of sex-matched controls (percentage of the mean HGS value [HGS%]). Multivariate linear regression analysis was used to assess HGS predictors. Competing risk regression analysis was used to evaluate 5-year all-cause mortality risk. Differences in survival time between HGS% tertiles were quantitated by analyzing the restricted mean survival time. RESULTS: The AUROC for HGS% was higher than the AUROCs for absolute or normalized HGS values. Compared with the high HGS% tertile, low HGS% (subdistribution hazard ratio [sHR] = 2.36; 95% CI, 1.19-3.70) and middle HGS% (sHR = 1.79; 95% CI, 1.12-2.74) tertiles were independently associated with higher all-cause mortality and those with high HGS% tertile survived on average 7.95 mo (95% CI, 3.61-12.28) and 18.99 mo (95% CI, 14.42-23.57) longer compared with middle and low HGS% tertile, respectively. CONCLUSIONS: HGS% was a strong predictor of all-cause mortality risk in incident dialysis patients and a better discriminator of survival than absolute HGS or HGS normalized to body size dimensions.

Real world use of tirzepatide in the treatment of type 2 diabetes in an Arab population.

AIM: Tirzepatide is a glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 (GLP-1) dual receptor agonist (RA) that reduces glycated haemoglobin (HbA1c) and weight in patients with type 2 diabetes. We assessed the effectiveness of tirzepatide in real-world use in an Arab population. METHODS: Review of clinical data from a specialist outpatient diabetes centre; study time points and outcome measures were pre-specified. RESULTS: Tirzepatide was initiated in 8945 patients between 24 October 2022 and 31 December 2023. Of these, 3686 individuals reached 40 weeks of follow-up. At initiation, the mean ± SD age was 54.1 ± 11.5 years, body mass index 34.6 ± 6.0 kg/m2 and HbA1c 7.3 ± 1.5% (56 ± 17 mmol/mol); 2296 (62%) were switched to tirzepatide from another GLP-RA and 317 (8.6%) reported previous bariatric surgery. The maximum dose dispensed was ≥12.5 mg/week in 1087, 7.5-10.0 mg/week in 1688 and 2.5-5.0 mg/week in 911. The mean 40-week reduction in HbA1c was 0.6 ± 1.2% (8 ± 13 mmol/mol) and the reduction in weight was 4.5 ± 6.9 kg (4.8 ± 7.3%). GLP-RA-naïve patients experienced a significantly greater reduction in HbA1c [1.0 ± 1.3% (11 ± 14 mmol/mol) versus 0.5 ± 1.2% (6 ± 13 mmol/mol), p < .0001] and weight (7.2 ± 8.6 vs. 4.2 ± 6.6 kg, p < .0001) compared with previously exposed individuals. Post-metabolic bariatric surgery patients lost significantly more weight (7.8 ± 9.4 vs. 4.5 ± 7.0 kg, p < .0001). Improvements in blood pressure, lipid profile, and liver transaminases were noted at 40 weeks. Tirzepatide was well tolerated, with 288 (7.8%) of patients discontinuing treatment because of adverse effects, predominantly gastrointestinal. CONCLUSION: In real-world use, tirzepatide significantly reduced HbA1c levels and weight and was well tolerated. Previous GLP-RA use was associated with significantly lesser HbA1c and weight reduction, and previous metabolic bariatric surgery was associated with greater weight loss.

Deep deterministic policy gradient algorithm: A systematic review.

Deep Reinforcement Learning (DRL) has gained significant adoption in diverse fields and applications, mainly due to its proficiency in resolving complicated decision-making problems in spaces with high-dimensional states and actions. Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based reinforcement learning methods. The aim of this study is to provide a thorough examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using relevant academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85 relevant studies published in the last five years (2018-2023). We provide a comprehensive overview of the key concepts and components of DDPG, including its formulation, implementation, and training. Then, we highlight the various applications and domains of DDPG, including Autonomous Driving, Unmanned Aerial Vehicles, Resource Allocation, Communications and the Internet of Things, Robotics, and Finance. Additionally, we provide an in-depth comparison of DDPG with other DRL algorithms and traditional RL methods, highlighting its strengths and weaknesses. We believe that this review will be an essential resource for researchers, offering them valuable insights into the methods and techniques utilized in the field of DRL and DDPG.

From theory to practice: An integrated TTF-UTAUT study on electric vehicle adoption behavior.

In Industry 4.0, the adoption of new technology has played a major role in the transportation sector, especially in the electric vehicles (EVs) domain. Nevertheless, consumer attitudes towards EVs have been difficult to gauge but researchers have tried to solve this puzzle. The prior literature indicates that individual attitudes and technology factors are vital to understanding users' adoption of EVs. Thus, the main aim is to meticulously investigate the unexplored realm of EV adoption within nations traditionally reliant on oil, exemplified by Saudia Arabia. By integrating the "task technology fit" (TTF) model and the "unified theory of acceptance and usage of technology" (UTAUT), this research develops and empirically validates the framework. A cross-section survey approach is adopted to collect 273 valid questionnaires from customers through convincing sampling. The empirical findings confirm that the integration of TTF and UTAUT positively promotes users' adoption of EVs. Surprisingly, the direct effect of TTF on behavioral intentions is insignificant, but UTAUT constructs play a significant role in establishing a significant relationship. Moreover, the UTAUT social influence factor has no impact on the EVs adoption. This groundbreaking research offers a comprehensive and holistic methodology for unravelling the complexities of EV adoption, achieved through the harmonious integration of two well-regarded theoretical frameworks. The nascent of this research lies in the skilful blending of technological and behavioral factors in the transportation sector.

Exogenous glutathione protected wheat seedling from high temperature and water deficit damages.

High temperatures (HT) and drought are two major factors restricting wheat growth in the early growth stages. This study investigated the role of glutathione (GSH) amendment (0.0, 0.5, 1.0, and 2.0 mM) to soil in mitigating the adverse effect of HT (33 °C, with 25 °C as a control), water regimes (60% of field capacity and control), and their combinations. HT decreased the length, project area, surface area, volume, and forks of the root, while drought had the reverse effect. Shoot length, leaf area, leaf relative water content, and shoot and root dry matter were significantly decreased by HT and drought, and their combined impact was more noticeable. GSH significantly promoted the root system, shoot growth, and leaf relative water content. The combined treatment reduced chlorophyll a, chlorophyll b, and total chlorophyll. However, 0.5 mM GSH raised chlorophyll a, chlorophyll b, and total chlorophyll by 28.6%, 41.4%, and 32.5%, respectively, relative to 0.0 mM GSH. At combined treatment, 0.5 mM GSH decreased malondialdehyde (MDA) by 29.5% and increased soluble protein content by 24.1%. GSH meaningfully enhanced the activity of superoxide dismutase, catalase, and ascorbate peroxide in different treatments. This study suggested that GSH could protect wheat seedlings from the adverse effects of HT and/or drought stresses.

Infarct Size Reduction in an Anterior ST-Elevation Myocardial Infarction Following "Optimized" Supersaturated Oxygen Therapy.

This comprehensive case report documents the treatment of a 37-year-old female patient who presented with anterior ST-elevation myocardial infarction (STEMI). The patient underwent percutaneous coronary intervention (PCI), followed by an innovative therapy - optimized supersaturated oxygen therapy (SSO2). This therapy was chosen due to its potential to enhance myocardial salvage, particularly in severe MI cases like the patient. The report meticulously details the patient's clinical course, including the diagnostic procedures and the rationale behind opting for SSO2 therapy. It highlights the significant improvements observed post-therapy: enhanced left ventricular (LV) function and a remarkable reduction in the size of the LV apical aneurysm. These outcomes suggest a direct benefit of SSO2 in reducing myocardial damage. Finally, the report discusses the broader implications of these findings. It underscores the potential of optimized SSO2 therapy in clinical settings, particularly for patients with anterior MI. The case exemplifies how advanced therapeutic interventions like SSO2 can play a pivotal role in improving clinical outcomes post-MI, thereby advocating for its consideration in similar clinical scenarios.

Flavonostilbenes natural hybrids from Rhamnoneuron balansae as potential antitumors targeting ALDH1A1: molecular docking, ADMET, MM-GBSA calculations and molecular dynamics studies.

Several studies have linked Cancer stem cells (CSCs) to cancer resistance development to chemotherapy and radiotherapy. ALDH1A1 is a key enzyme that regulates the gene expression of CSCs and creates an immunosuppressive tumor microenvironment. It was reported that quercetin and resveratrol were among the inhibitors of ALDH1A1. In early 2022, it was reported that new 11 flavonostilbenes (rhamnoneuronal D-N) were isolated from Rhamnoneuron balansae as potential antiaging natural products. Rhamnoneuronal H (5) could be envisioned as a natural hybrid of quercetin and resveratrol. It was therefore hypothesized that 5 and its analogous isolates rhamnoneuronal D-G (1-4) and rhamnoneuronal I-N (6-11) would have potential ALDH1A1 inhibitory activity. To this end, all isolates were subjected to molecular docking, MM-GBSA, ADMET, and molecular dynamics simulations studies to assess their potential as new leads for cancer treatment targeting ALDH1A1. In silico findings revealed that natural hybrid 5 has a similar binding affinity, judged by MM-GBSA, to the ALDH1A1 active site when compared to the co-crystalized ligand (-64.71 kcal/mole and -64.12 kcal/mole, respectively). Despite having lesser affinity than that of the co-crystalized ligand, the rest of the flavonostilbenes, except 2-4, displayed better binding affinities (-37.55 kcal/mole to -58.6 kcal/mole) in comparison to either resveratrol (-34.44 kcal/mole) or quercetin (-36.48 kcal/mole). Molecular dynamic simulations showed that the natural hybrids 1, 5-11 are of satisfactory stability up to 100 ns. ADMET outcomes indicate that these hybrids displayed acceptable properties and hence could represent an ideal starting point for the development of potent ALDH1A1 inhibitors for cancer treatment.Communicated by Ramaswamy H. Sarma.

An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.

Alzheimer's disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer's disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer's disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer's disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.

Integrated approaches for increasing plant yield under salt stress.

Salt stress affects large cultivated areas worldwide, thus causing remarkable reductions in plant growth and yield. To reduce the negative effects of salt stress on plant growth and yield, plant hormones, nutrient absorption, and utilization, as well as developing salt-tolerant varieties and enhancing their morpho-physiological activities, are some integrative approaches to coping with the increasing incidence of salt stress. Numerous studies have been conducted to investigate the critical impacts of these integrative approaches on plant growth and yield. However, a comprehensive review of these integrative approaches, that regulate plant growth and yield under salt stress, is still in its early stages. The review focused on the major issues of nutrient absorption and utilization by plants, as well as the development of salt tolerance varieties under salt stress. In addition, we explained the effects of these integrative approaches on the crop's growth and yield, illustrated the roles that phytohormones play in improving morpho-physiological activities, and identified some relevant genes involve in these integrative approaches when the plant is subjected to salt stress. The current review demonstrated that HA with K enhance plant morpho-physiological activities and soil properties. In addition, NRT and NPF genes family enhance nutrients uptake, NHX1, SOS1, TaNHX, AtNHX1, KDML, RD6, and SKC1, maintain ion homeostasis and membrane integrity to cope with the adverse effects of salt stress, and sd1/Rht1, AtNHX1, BnaMAX1s, ipal-1D, and sft improve the plant growth and yield in different plants. The primary purpose of this investigation is to provide a comprehensive review of the performance of various strategies under salt stress, which might assist in further interpreting the mechanisms that plants use to regulate plant growth and yield under salt stress.

Region-wise evaluation of price-based demand response programs in Japan's wholesale electricity market considering microeconomic equilibrium.

Real-time pricing demand response programs (RTP-DRPs) are practical measures that ensure the end user's profitability from using electricity by adjusting the supply and demand equilibrium without activating costly solutions. This study explores the potential of RTP-DRPs by developing and applying a region-wise modeling approach based on maximizing the end user's social welfare in the wholesale electricity market in Japan. The regions of the wholesale market are classified based on their response into regions with excess supply, regions with high demand burden, and regular suppliers of inter-regional connections. The results revealed that the RTP-DRPs could potentially reduce the peak demand of the residential sector in Chubu, Chugoku, Kansai, Kyushu, Tokyo, and Tohoku by 1.91%-7.81%. Meanwhile, in Hokkaido, Hokuriku, and Shikoku, by 16.13%-22.9%. The avoided greenhouse emission (GHG) in Tokyo is estimated to be 82.6 and 192.2 tons in summer and winter, respectively.

Interleukin-6-to-Albumin Ratio as a Superior Predictor of Mortality in End-Stage Kidney Disease Patients.

INTRODUCTION: In patients with chronic kidney disease (CKD), high interleukin-6 (IL-6) and low albumin circulating concentrations are associated with worse outcomes. We examined the IL-6-to-albumin ratio (IAR) as a predictor of risk of death in incident dialysis patients. METHODS: In 428 incident dialysis patients (median age 56 years, 62% men, 31% diabetes mellitus, 38% cardiovascular disease [CVD]), plasma IL-6 and albumin were measured at baseline to calculate IAR. We compared the discrimination of IAR with other risk factors for predicting 60-month mortality using receiver operating characteristic curve (ROC) and analyzed the association of IAR with mortality using Cox regression analysis. We divided patients into IAR tertiles and analyzed: (1) cumulative incidence of mortality and the association of IAR with mortality risk in Fine-Gray analysis, taking kidney transplantation as competing risk and (2) the restricted mean survival time (RMST) to 60-month mortality and differences of RMST (∆RMST) between IAR tertiles to describe quantitative differences of survival time. RESULTS: For all-cause mortality, the area under the ROC curve (AUC) for IAR was 0.700, which was greater than for IL-6 and albumin separately, while for CV mortality, the AUC for IAR (0.658) showed negligible improvement over IL-6 and albumin separately. In Cox regression analysis, IAR was significantly associated with all-cause mortality but not with CV mortality. Both high versus low and middle versus low tertiles of IAR associated with higher risk of all-cause mortality, subdistribution hazard ratio of 2.22 (95% CI 1.40-3.52) and 1.85 (95% CI 1.16-2.95), respectively, after adjusting for age, sex, diabetes mellitus, CVD, smoking, and estimated glomerular filtration rate. ∆RMST at 60 months showed significantly shorter survival time in middle and high IAR tertiles compared with low IAR tertile for all-cause mortality. CONCLUSIONS: Higher IAR was independently associated with significantly higher all-cause mortality risk in incident dialysis patients. These results suggest that IAR may provide useful prognostic information in patients with CKD.

Dehiscent Prosthetic Aortic Valve and Aortic Root Pseudoaneurysm Complicated by Left Main Coronary Artery Compression.

Prosthetic aortic valve dehiscence is an uncommon complication of prosthetic valve endocarditis that may occur in patients who have undergone aortic valve replacement (AVR). The concurrent presence of aortic root pseudoaneurysm may further complicate the clinical presentation through the external compression of coronary arteries. Thus, patients may present with clinical features of coronary ischemia. Echocardiogram and coronary angiography are useful in establishing diagnosis. Treatment involves a multidisciplinary approach involving cardiologists, infectious disease specialists, and cardiothoracic surgeons. The authors of this study discuss a 51-year-old male who presented with anginal chest pain and was found to have a new left bundle branch block, elevated troponins, and left main coronary artery compression complicating aortic root aneurysm. He ended up requiring a re-do AVR, repair of the pseudoaneurysm, and coronary artery bypass graft.

Central obesity as assessed by conicity index and a-body shape index associates with cardiovascular risk factors and mortality in kidney failure patients.

Background: Anthropometric indices of central obesity, waist circumference (WC), conicity index (CI), and a-body shape index (ABSI), are prognostic indicators of cardiovascular (CV) risk. The association of CI and ABSI with other CV risk indices, markers of nutritional status and inflammation, and clinical outcomes in chronic kidney disease (CKD) stage 5 (CKD5) patients was investigated. Methods: In a cross-sectional study with longitudinal follow up of 203 clinically stable patients with CKD5 (median age 56 years; 68% males, 17% diabetics, 22% with CV disease, and 39% malnourished), we investigated CI and ABSI and their associations with atherogenic index of plasma (AIP), Framingham CV risk score (FRS), Agatston scoring of coronary artery calcium (CAC) and aortic valve calcium (AVC), handgrip strength (HGS), high sensitivity C-reactive protein (hsCRP) and interleukin-6 (IL-6). CV events (CVE) and all-cause mortality during up to 10-years follow up were analyzed by multivariate survival analysis of restricted mean survival time (RMST). Results: Chronic kidney disease patients with middle and highest CI and ABSI tertiles (indicating greater abdominal fat deposition), compared to those with the lowest CI and ABSI tertiles, tended to be older, more often men and diabetic, had significantly higher levels of hsCRP, IL-6, AIP, FRS, CAC and AVC scores. CI and ABSI were positively correlated with CAC, FRS, AIP, hsCRP and IL-6. Both CI and ABSI were negatively correlated with HGS. In age-weighted survival analysis, higher CI and ABSI were associated with higher risk of CVE (Wald test = 4.92, p = 0.027; Wald test = 4.95, p = 0.026, respectively) and all-cause mortality (Wald test = 5.24, p = 0.022; Wald test = 5.19, p = 0.023, respectively). In RMST analysis, low vs. high and middle tertiles of CI and ABSI associated with prolonged CVE-free time and death-free time, and these differences between groups increased over time. Conclusion: Abdominal fat deposit indices, CI and ABSI, predicted CV outcomes and all-cause mortality, and were significantly associated with the inflammatory status in CKD patients.

The Multiscale Surface Vision Transformer.

Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, notably for structures where the translation of the convolution operation is non-trivial, the quadratic cost of the self-attention operation remains an obstacle for many dense prediction tasks. Inspired by some of the latest advances in hierarchical modelling with vision transformers, we introduce the Multiscale Surface Vision Transformer (MS-SiT) as a backbone architecture for surface deep learning. The self-attention mechanism is applied within local-mesh-windows to allow for high-resolution sampling of the underlying data, while a shifted-window strategy improves the sharing of information between windows. Neighbouring patches are successively merged, allowing the MS-SiT to learn hierarchical representations suitable for any prediction task. Results demonstrate that the MS-SiT outperforms existing surface deep learning methods for neonatal phenotyping prediction tasks using the Developing Human Connectome Project (dHCP) dataset. Furthermore, building the MS-SiT backbone into a U-shaped architecture for surface segmentation demonstrates competitive results on cortical parcellation using the UK Biobank (UKB) and manually-annotated MindBoggle datasets. Code and trained models are publicly available at https://github.com/metrics-lab/surface-vision-transformers.

Validation of the new IDF-DAR risk assessment tool for Ramadan fasting in patients with diabetes.

BACKGROUND: A new IDF-DAR (International Diabetes Federation - Diabetes and Ramadan Alliance) risk stratification tool was published in 2021 to better stratify the risk of Ramadan fasting in people with diabetes. METHODS: We performed a prospective, survey-based study before and after Ramadan 1442/2021 to explore the ability of the new IDF-DAR risk stratification tool to predict the probability of fasting and the risk of complications from fasting in people with diabetes. RESULTS: A pre-Ramadan assessment was completed for 659 patients who intended to fast in Ramadan; 647(98.2%) answered the post-Ramadan follow-up questionnaire. Mean age was 53.5 years and 47.9% were females. 603(91.5%) had type 2 diabetes while 56(8.5%) had type 1 diabetes. Using the IDF-DAR risk criteria at the pre-Ramadan assessment, 339(51.4%) were categorized as low-risk (score <3), 173(26.3%) as moderate-risk (score 3.5-6) and 147(22.3%) as high-risk (score >6). 94.3%, 81.1% and 76.9% patients fasted the full 30 days in the low, moderate and high risk groups respectively (p < 0.0001). Any hypoglycaemia was reported in the low, moderate and high risk groups by 6.3%, 21.9% and 35.0% respectively while severe hypoglycaemia was reported by 3(2.1%) patients in the high, 3(1.8%) in the moderate and none(0%) in the low risk groups. Hyperglycaemia (>250 mg/dL) was reported in the low, moderate and high risk groups by 2.7%, 13.0% and 23.8% respectively. CONCLUSION: The new IDF-DAR risk assessment tool appears to reliably predict both the ability to fast during Ramadan as well as the likelihood of getting hypoglycaemia or hyperglycaemia.

Prevalence of vulvovaginal candidiasis among pregnant women in Africa: A systematic review and meta-analysis.

INTRODUCTION: Vulvovaginal candidiasis (VVC) is a yeast infection of the vulva, which is caused by Candida species and affects women worldwide. Pregnant women are more vulnerable to VVC due to certain risks. Moreover, their offspring are also exposed to the risk of preterm birth. In this context, ascertaining the burden of VVC is of paramount importance and this meta-analysis was conducted to estimate the occurrence of VVC among pregnant women in Africa. METHODOLOGY: Database search was carried out through PubMed, Scopus, Science-Direct, and Google Scholar from the date of inception until December 2020. All the studies on the prevalence of VVC among African pregnant women were included in the analysis. The pooled prevalence was estimated based on the Random-effect model DerSimonian-Laird approach with Freeman- Tukey double arcsine transformed proportion. Heterogeneity was assessed using I2 test and subsequently explored using subgroup and meta-regression analysis. RESULTS: A total of Sixteen records having a sample size 4,185 were included in this study. The overall prevalence of VVC was pooled at 29.2% (CI 95%: 23.4 - 33.0). Subgroup analysis revealed a higher prevalence in Eastern Africa, followed by Western Africa and North Africa (35%, 28%, and 15% respectively). Moderator analysis indicated that the studies that used advanced methods of detection had a higher prevalence (p = 0.048). In addition, the large sample size was associated with higher prevalence (p ≤ 0.001). No other moderators were found to be statistically significant. CONCLUSIONS: The overall prevalence of VVC among African pregnant women is comparable to other studies worldwide. However, appropriate identification techniques and larger sample size could likely be associated with an increased prevalence. Our findings necessitate the need for further investigations to determine the geographical distribution of VVC across African regions.

Investigation of healthcare-associated SARS-CoV-2 infection: Learning outcomes from an investigative process in the initial phase of the pandemic.

Background: Healthcare-associated (HCA) SARS-CoV-2 infection is a significant contributor to the spread of the 2020 pandemic. Timely review of HCA cases is essential to identify learning to inform infection prevention and control (IPC) policies and organisational response. Aim: To identify key areas for improvement through rapid investigation of HCA SARS-CoV-2 cases and to implement change. Methods: Cases were identified based on date of first positive SARS-CoV-2 PCR sample in relation to date of hospital admission. Cases were reviewed using a structured gap analysis tool to identify key learning points. These were discussed in weekly multidisciplinary meetings to gain consensus on learning outcomes, level of harm incurred by the patient and required actions. Learning was then promptly fed back to individual teams and the organisation. Findings: Of the 489 SARS-CoV-2 cases admitted between 10th March and 23rd June 2020, 114 suspected HCA cases (23.3%) were reviewed; 58/489 (11.8%) were ultimately deemed to be HCA. Five themes were identified: individual patient vulnerability, communication, IPC implementation, policy issues and organisational response. Adaptations to policies based on these reviews were completed within the course of the initial phase of the pandemic. Conclusion: This approach enabled timely learning and implementation of control measures and policy development.

Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches.

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.