Explore the vast range of titles available.

Middle East respiratory syndrome coronavirus (MERS-CoV) infection is associated with high mortality and has no approved antiviral therapy. We aimed to compare ribavirin and interferon alfa-2a treatment for patients with severe MERS-CoV infection with a supportive therapy only. In this retrospective cohort study, we included adults (aged ≥16 years) with laboratory-confirmed MERS-CoV infection and pneumonia needing ventilation support, diagnosed between Oct 23, 2012, and May 1, 2014, at the Prince Sultan Military Medical City (Riyadh, Saudi Arabia). All patients received appropriate supportive care and regular clinical and laboratory monitoring, but patients diagnosed after Sept 16, 2013, were also given oral ribavirin (dose based on calculated creatinine clearance, for 8–10 days) and subcutaneous pegylated interferon alfa-2a (180 μg per week for 2 weeks). The primary endpoint was 14-day and 28-day survival from the date of MERS-CoV infection diagnosis. We used χ2 and Fischer's exact test to analyse categorical variables and the t test to analyse continuous variables. We analysed 20 patients who received ribavirin and interferon (treatment group; initiated a median of 3 days [range 0–8] after diagnosis) and 24 who did not (comparator group). Baseline clinical and laboratory characteristics were similar between groups, apart from baseline absolute neutrophil count, which was significantly lower in the comparator group (5·88 × 109/L [SD 3·95] vs 9·88 × 109/L [6·63]; p=0·023). 14 (70%) of 20 patients in the treatment group had survived after 14 days, compared with seven (29%) of 24 in the comparator group (p=0·004). After 28 days, six (30%) of 20 and four (17%) of 24, respectively, had survived (p=0·054). Adverse effects were similar between groups, apart from reduction in haemoglobin, which was significantly greater in the treatment group than in the comparator group (4·32 g/L [SD 2·47] vs 2·14 g/L [1·90]; p=0·002). In patients with severe MERS-CoV infection, ribavirin and interferon alfa-2a therapy is associated with significantly improved survival at 14 days, but not at 28 days. Further assessment in appropriately designed randomised trials is recommended. None.

Background Middle East respiratory syndrome coronavirus infection is associated with high mortality rates but limited clinical data have been reported. We describe the clinical features and outcomes of patients admitted to an intensive care unit (ICU) with Middle East respiratory syndrome coronavirus (MERS-CoV) infection. Methods Retrospective analysis of data from all adult (>18 years old) patients admitted to our 20-bed mixed ICU with Middle East respiratory syndrome coronavirus infection between October 1, 2012 and May 31, 2014. Diagnosis was confirmed in all patients using real-time reverse transcription polymerase chain reaction on respiratory samples. Results During the observation period, 31 patients were admitted with MERS-CoV infection (mean age 59 ± 20 years, 22 [71 %] males). Cough and tachypnea were reported in all patients; 22 (77.4 %) patients had bilateral pulmonary infiltrates. Invasive mechanical ventilation was applied in 27 (87.1 %) and vasopressor therapy in 25 (80.6 %) patients during the intensive care unit stay. Twenty-three (74.2 %) patients died in the ICU. Nonsurvivors were older, had greater APACHE II and SOFA scores on admission, and were more likely to have received invasive mechanical ventilation and vasopressor therapy. After adjustment for the severity of illness and the degree of organ dysfunction, the need for vasopressors was an independent risk factor for death in the ICU (odds ratio = 18.33, 95 % confidence interval: 1.11–302.1, P  = 0.04). Conclusions MERS-CoV infection requiring admission to the ICU is associated with high morbidity and mortality. The need for vasopressor therapy is the main risk factor for death in these patients.

Background. The Middle East respiratory syndrome (MERS) coronavirus causes isolated cases and outbreaks of severe respiratory disease. Essential features of the natural history of disease are poorly understood. Methods. We studied 37 adult patients infected with MERS coronavirus for viral load in the lower and upper respiratory tracts (LRT and URT, respectively), blood, stool, and urine. Antibodies and serum neutralizing activities were determined over the course of disease. Results. One hundred ninety-nine LRT samples collected during the 3 weeks following diagnosis yielded virus RNA in 93% of tests. Average (maximum) viral loads were 5 × 106 (6 × 1010) copies/mL. Viral loads (positive detection frequencies) in 84 URT samples were 1.9 × 104 copies/mL (47.6%). Thirty-three percent of all 108 serum samples tested yielded viral RNA. Only 14.6% of stool and 2.4% of urine samples yielded viral RNA. All seroconversions occurred during the first 2 weeks after diagnosis, which corresponds to the second and third week after symptom onset. Immunoglobulin M detection provided no advantage in sensitivity over immunoglobulin G (IgG) detection. All surviving patients, but only slightly more than half of all fatal cases, produced IgG and neutralizing antibodies. The levels of IgG and neutralizing antibodies were weakly and inversely correlated with LRT viral loads. Presence of antibodies did not lead to the elimination of virus from LRT. Conclusions. The timing and intensity of respiratory viral shedding in patients with MERS closely matches that of those with severe acute respiratory syndrome. Blood viral RNA does not seem to be infectious. Extrapulmonary loci of virus replication seem possible. Neutralizing antibodies do not suffice to clear the infection.

The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.

This study investigated the pathophysiology of polycystic ovarian syndrome (PCOS) and evaluated the protective effects of various treatments in immature female Wistar rats (  = 48). The rats were divided into 6 groups: Olive oil injection (negative control, G1); testosterone propionate (TP)-induced PCOS (G2); probiotic + TP (G3); myo-inositol (myo-ins) + TP (G4); extract + TP (G5); extract + TP (G6). The body weight, body weight gain, and percentage gain were measured weekly and then transformed using the base-10 logarithm (log ). TNF-α, IL-4, IL-10, and IL-17 were weekly measured using ELISA kits. Glutathione peroxidase (GSH-Px), superoxide dismutase (SOD), and catalase (CAT) were analysed in the serum and liver extracts. The significantly reduced the TNF-α levels (  < 0.01). The probiotic and myo-ins significantly elevated the IL-10 levels (  < 0.01). Both plant extracts moderately restored the IL-10 levels. The probiotic and administration significantly reduced the IL-17 levels (  < 0.01). The administration also decreased the IL-17 levels, though the effect was less pronounced than that of  . The probiotic, myo-ins, and groups exhibited enhanced CAT activity (  < 0.05). showed significant increases in the SOD and GSH-Px activities (  < 0.01), showing the most dramatic improvement. The use of these four treatments as a monotherapy in this study resulted in different changes. Therefore, further investigation is necessary to evaluate the protective effects of combining duos or trios of these treatments against this disease.

Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance of the model was measured using several metrics including accuracy, precision, and recall. The obtained results were compared with those obtained from the existing works. The comparison shows that the proposed model outperformed the existing solutions in terms of accuracy, precision, recall, and loss performance, which indicates that the adaptive early stopping coupled with GCE improved the ability of DenseNet169 to accurately detect knee OA.

This study aimed to evaluate the water sorption, solubility, and translucency of 3D-printed denture base resins (NextDent, FormLabs, and Asiga), compare them to heat-polymerized acrylic denture base resins, and assess their performance under the effects of thermal cycling. A total of 80 acrylic disc specimens were used in the current study, categorized into four groups (n = 10); in one group, the samples were fabricated conventionally with a heat-polymerizing process (control), while the other three groups were fabricated digitally from different 3D-printed reins (NextDent, FormLabs, and Asiga). Specimens were fabricated according to the manufacturers’ recommendations and immersed in distilled water for 48 h at 37 °C. Data on water sorption, solubility, and translucency measurements (T1) were obtained. All the specimens were subjected to 5000 thermal cycles, and then the measures were repeated using the same method (T2). Data analysis was attained via ANOVA and the post hoc Tukey test (α = 0.05). The type of resin significantly affected the values of water sorption, solubility, and translucency (p < 0.001). The water sorption of 3D-printed resins was increased significantly in comparison to control with or without a thermal cycling effect. In terms of solubility, a significant increase in 3D-printed resins before thermocycling was observed; however, after thermocycling, Asiga had a significantly low value compared to the other groups (p < 0.001). Thermal cycling increased the water sorption and solubility of all tested materials. In comparison to control, the translucency of the 3D-printed resins was significantly decreased (p < 0.001). The translucency was significantly decreased per material in terms of the thermal cycling effect (before and after). NextDent showed significantly low translucency values (p < 0.001) compared to the other groups. All 3D-printed resin groups had higher water sorption and solubility and lower translucency values in comparison to the heat-polymerized resin group. Regardless of resin types, thermal cycling adversely affected all tested properties.

Purpose The COVID-19 pandemic might add to the stressors experienced by people living with rheumatic diseases. This study aimed to examine rheumatic patients’ functional and psychosocial states during the pandemic and assess its impact on their quality of life. Methods Our time-series study included a patient-centered electronic survey, sampling adult rheumatic patients living in Saudi Arabia at different time points from March to August 2020. Patient-reported outcomes included physical function, anxiety, depression, fatigue, sleep disturbance, ability to participate in social roles, and pain interference domains were measured using the Patient-Reported Outcomes Measurement Information System (PROMIS-29 Profile v2.1). Results A total of 1278 respondents were enrolled. Results showed significant variation in patients’ experiences. Our analyses revealed that the physical well-being of rheumatic patients was significantly impacted, and such effect was persistent over time irrespective of public health measures to control the COVID-19 outbreak. Conclusion Our findings consistently demonstrated the need for psychological and social consideration to improve rheumatic patients’ quality of life. Nevertheless, there is still a lot to be learned about the extent of COVID-19 impact on rheumatic patients and the implications it has on long-term disease outcomes.

Recognition of human actions based on visual input presents significant challenges due to the diverse ways in which individuals perform identical actions, the temporal fluctuations inherent in these actions, and the variations in perspectives from which they are observed. To overcome the constraints associated with single-modality approaches, researchers have increasingly turned to multimodal visual data fusion techniques. This study introduces an optimized deep learning model, designed for action recognition by integrating various data modalities, including skeletal and inertial data. The model features an innovative 1D convolutional block with residual connections aimed at addressing the vanishing gradient issue, thereby supporting the training of deeper networks and facilitating the extraction of hierarchical features across different temporal scales. Furthermore, a Gram Matrix-based attention mechanism is introduced, which utilizes the similarity among feature vectors over time to create an attention map, thereby improving the model’s capacity to evaluate the significance of long-range temporal dependencies. The model’s efficacy is thoroughly assessed using the publicly accessible UTD-MHAD and CZU-MHAD datasets, revealing superior performance relative to current leading action recognition methodologies with accuracy 93.5% and 94.6% for fused skeleton-inertial on UTD-MHAD and CZU-MHAD datasets respectively. Comprehensive evaluation methods, including confusion matrices, ROC curves, t-SNE visualizations, Grad-CAM heatmaps and attention-kinematic contribution, are employed to validate the model’s robustness and provide insights into its decision-making process. The findings highlight the model’s effectiveness in leveraging multi-modal data fusion and advanced temporal feature extraction, offering a promising approach for improving action recognition tasks.

Background. This study aimed to evaluate the effects of different denture cleansing solutions (DCSs) on the retention of Locator and Locator R-Tx attachment systems of implant retained overdentures (IRO). Methods. Two part acrylic resin blocks were fabricated, upper part contained metal housing and plastic inserts and lower part contained implant analogs and abutments. Eighty pink plastic inserts (40/attachment, 10/solution) were immersed in Corega, Fittydent, sodium hypochlorite, and water for a time simulating upto 1-year of clinical usage. Acrylic blocks were held on a universal testing machine for a pull-out test to record the dislodgement force. Measurements were conducted after 6 months (T1) and 12 months (T2). One-way ANOVA followed by Tukey’s HSD test was used to analyze the results (α = 0.05). Results. For both attachments, retention significantly decreased after immersion in different solutions at T2 P<0.001. Locator R-Tx attachment in NaOCl showed a significant decrease in retention compared with other solutions at T1. At T2, there was a significant decrease in retention for all DCS compared with water P<0.001. Locator R-TX showed higher retention values per solution compared to Locator attachment P<0.001. In terms of retention loss %, NaOCl recorded the highest (61.87%) loss, followed by Corega (55.54%) and Fittydent (43.13%), whereas water demonstrated the best retention (16.13%) in both groups. Conclusion. Locator R-TX has better retention with different DCS immersion. The loss of retention varied with different types of DCS and NaOCl recorded the highest retention loss. Therefore, denture cleanser selection must be guided by the type of IRO attachment.