INFLUENCE OF DIFFERENT TESTING POSTURES ON HAND GRIP STRENGTH

Hand grip strength (HGS) is a useful, functional measure of the integrity of upper extremity, however many studies examined it from selected positions (supine, sitting, standing), with no emphasis on other derived positions that are used in a clinical setting. The objective was to evaluate HGS in different body positions that are used in a clinical setting by using a standard protocol. A convenience sample of 40 healthy male participants was recruited for this study, with no history of psychiatric or neurological dysfunction, or upper extremity orthopedic dysfunction. Grip strength was measured in the dominant hand with Jamar Plus+ digital hand dynamometer in five positions: supine, prone, side-lying, sitting and standing. The HGS value in the prone position was significantly lower than that in standing position (p = 0.043) and the sitting position (p = 0.013). However, no statistical difference was found in HGS among supine, prone, side-lying positions. Grip strength correlated moderately with age (r = 0.643). This study provides a useful evaluation of grip strength in different positions. Using identical upper extremity positions, grip strength is variable among different body positions. Grip strength is equivalent when tested from the supine, side-lying or prone, thus position can be adjusted according to the patient's condition. Finally, Age is one of the important determinants of the hand grip evaluation, particularly when standing position is used

Antibody responses to acute COVID-19 infection; assessment via multiplex LABScreen COVID Plus Assay

Background: Understanding the profile of antibody responses following acute COVID-19 infection is required. Aim: to describe the pattern of IgG anti-COVID-19 antibody production in patients with acute infection using the LABScreen COVID Plus assay. Results: The overall seropositivity was 69/73(94.5%). Anti-Spike, Spike 1 and spike S2 subunits were positive in 78.1%, while anti spike receptor binding domain  (RBD) was detected in 68.4% and anti nucleocapsid protein in 61.6%. The overall positivity of the assay reached 100.0% during the second week post symptoms. The mean fluorescent intensities (MFI) of anti-Spike S1 was higher in the second week than the first week, p < /em>=0.03. MFI of anti-Spike S2 was significantly higher in PCR positive patients in comparison with the negative ones, p < /em>=0.006. When compared to the RT-PCR results; the overall antibodies positivity, anti-Spike, and anti-Spike2 antibodies had sensitivities (100% and 84.7%) and specificities (28.6% and 50.0%) and accuracies (86.3% and 78.1%).  Patients' outcome correlated significantly with the time of hospital admission, p < /em>=0.001. Conclusion: COVID-19 IgG antibodies are detectable with considerable frequencies during the first two weeks post infection. Anti S2 antibodies correlates well with the RT-PCR results. The LABScreen COVID Plus is a sensitive assay for the detection of post-acute COVID-19 infection antibody responses.Background: Understanding the profile of antibody responses following acute COVID-19 infection is required. Aim: to describe the pattern of IgG anti-COVID-19 antibody production in patients with acute infection using the LABScreen COVID Plus assay. Results: The overall seropositivity was 69/73(94.5%). Anti-Spike, Spike 1 and spike S2 subunits were positive in 78.1%, while anti spike receptor binding domain  (RBD) was detected in 68.4% and anti nucleocapsid protein in 61.6%. The overall positivity of the assay reached 100.0% during the second week post symptoms. The mean fluorescent intensities (MFI) of anti-Spike S1 was higher in the second week than the first week, p < /em>=0.03. MFI of anti-Spike S2 was significantly higher in PCR positive patients in comparison with the negative ones, p < /em>=0.006. When compared to the RT-PCR results; the overall antibodies positivity, anti-Spike, and anti-Spike2 antibodies had sensitivities (100% and 84.7%) and specificities (28.6% and 50.0%) and accuracies (86.3% and 78.1%).  Patients' outcome correlated significantly with the time of hospital admission, p < /em>=0.001. Conclusion: COVID-19 IgG antibodies are detectable with considerable frequencies during the first two weeks post infection. Anti S2 antibodies correlates well with the RT-PCR results. The LABScreen COVID Plus is a sensitive assay for the detection of post-acute COVID-19 infection antibody responses

Skin prick test results and total ige levels of asthma patients in Zagazig University Hospital (2015-2019)

Background: Bronchial asthma is one of the relevant diseases of the respiratory tract, the asthma is one of the forms of respiratory allergy. The change in environment and aeroallergens are the main etiology of asthma. Allergy of asthma is thought to affect the bronchial region of the respiratory airway.Objective: The current study aimed to compare the total IgE elevation and skin prick test (SPT) positivity to evaluate the IgE and SPT ability to assess the asthmatic severity.Subjects and Methods: This retrospective case study was carried out on 3450 cases at the outpatient clinic and Chest Department at Zagazig University and did skin prick test and IgE in authorized centers were collected from 2015 -2019. The cases were already diagnosed asthmatic according to Global Initiative for Asthma (GINA) (9) guidelines and were divided according to severity into mild, moderate, and severe according to GINA guidelines.Results: IgE has been tested for different aeroallergens and has strong significant elevation with (P< 0.001) in Alternaria, cat hair, cotton, birch, and helminths aeroallergen. Most of the cases with elevated IgE had moderate asthma severity followed by mild severity cases then severe cases.Conclusion: Comparing the results of SPT and IgE, the SPT test is more accurate, reliable, and easy in detecting the aeroallergen sensitivity

CNN-Based Health Model for Regular Health Factors Analysis in Internet-of-Medical Things Environment

Remote health monitoring applications with the advent of Internet of Things (IoT) technologies have changed traditional healthcare services. Additionally, in terms of personalized healthcare and disease prevention services, these depend primarily on the strategy used to derive knowledge from the analysis of lifestyle factors and activities. Through the use of intelligent data retrieval and classification models, it is possible to study disease, or even predict any abnormal health conditions. To predict such abnormality, the Convolutional neural network (CNN) model is used, which can detect the knowledge related to disease prediction accurately from unstructured medical health records. However, CNN uses a large amount of memory if it uses a fully connected network structure. Moreover, the increase in the number of layers can lead to an increase in the complexity analysis of the model. Therefore, to overcome these limitations of the CNN-model, we propose a CNN-regular target detection and recognition model based on the Pearson Correlation Coefficient and regular pattern behavior, where the term "regular" denotes objects that generally appear in similar contexts and have structures with low variability. In this framework, we develop a CNN-regular pattern discovery model for data classification. First, the most important health-related factors are selected in the first hidden layer, then in the second layer, a correlation coefficient analysis is conducted to classify the positively and negatively correlated health factors. Moreover, regular patterns' behaviors are discovered through mining the regular pattern occurrence among the classified health factors. The output of the model is subdivided into regular-correlated parameters related to obesity, high blood pressure, and diabetes. Two distinct datasets are adopted to mitigate the effects of the CNN-regular knowledge discovery model. The experimental results show that the proposed model has better accuracy, and low computational load, compared with three different machine learning techniques methods

Architected poly(lactic acid)/poly(ε-caprolactone)/halloysite nanotube composite scaffolds enabled by 3D printing for biomedical applications

Herein, we report the physicochemical, thermal, mechanical and biological characteristics, including bioactivity, biodegradation and cytocompatibility of additive manufacturing-enabled novel nanocomposite scaffolds. The scaffolds comprise a blend of polylactic acid (PLA) and poly-ε-caprolactone (PCL) reinforced with halloysite nanotubes (HNTs). The nanoengineered filaments were developed by melt blending, and the nanocomposite scaffolds were manufactured by fused filament fabrication. Uniform dispersion of HNTs in the PLA/PCL blend is revealed via scanning electron microscopy. Mechanical property loss due to the addition of PCL to realize a suitable biodegradation rate of PLA was fully recovered by the addition of HNTs. Bioactivity, as revealed by the fraction of apatite growth quantified from XRD analysis, was 5.4, 6.3, 6.8 and 7.1% for PLA, 3, 5 and 7 wt% HNT in PLA/PCL blend, respectively, evidencing enhancement in the bioactivity. The degradation rate, in terms of weight loss, was reduced from 4.6% (PLA) to 1.3% (PLA/PCL) upon addition of PCL, which gradually increased to 4.4% by the addition of HNTs (at 7 wt% HNT). The results suggest that the biodegradation rate, mechanical properties and biological characteristics, including cytocompatibility and cell adhesion, of the 3D printed, microarchitected PLA/PCL/HNT composite scaffolds can be tuned by an appropriate combination of HNT and PCL content in the PLA matrix, demonstrating their promise for bone replacement and regeneration applications

Malware threats and detection for industrial mobile-IoT networks

Industrial IoT networks deploy heterogeneous IoT devices to meet a wide range of user requirements. These devices are usually pooled from private or public IoT cloud providers. A significant number of IoT cloud providers integrate smartphones to overcome the latency of IoT devices and low computational power problems. However, the integration of mobile devices with industrial IoT networks exposes the IoT devices to significant malware threats. Mobile malware is the highest threat to the security of IoT data, user\u27s personal information, identity, and corporate/financial information. This paper analyzes the efforts regarding malware threats aimed at the devices deployed in industrial mobile-IoT networks and related detection techniques. We considered static, dynamic, and hybrid detection analysis. In this performance analysis, we compared static, dynamic, and hybrid analyses on the basis of data set, feature extraction techniques, feature selection techniques, detection methods, and the accuracy achieved by these methods. Therefore, we identify suspicious API calls, system calls, and the permissions that are extracted and selected as features to detect mobile malware. This will assist application developers in the safe use of APIs when developing applications for industrial IoT networks

Insect peptide metchnikowin confers on barley a selective capacity for resistance to fungal ascomycetes pathogens

The potential of metchnikowin, a 26-amino acid residue proline-rich antimicrobial peptide synthesized in the fat body of Drosophila melanogaster was explored to engineer disease resistance in barley against devastating fungal plant pathogens. The synthetic peptide caused strong in vitro growth inhibition (IC50 value ∼1 μM) of the pathogenic fungus Fusarium graminearum. Transgenic barley expressing the metchnikowin gene in its 52-amino acid pre-pro-peptide form under the control of the inducible mannopine synthase (mas) gene promoter from the Ti plasmid of Agrobacterium tumefaciens displayed enhanced resistance to powdery mildew as well as Fusarium head blight and root rot. In response to these pathogens, metchnikowin accumulated in plant apoplastic space, specifying that the insect signal peptide is functional in monocotyledons. In vitro and in vivo tests revealed that the peptide is markedly effective against fungal pathogens of the phylum Ascomycota but, clearly, less active against Basidiomycota fungi. Importantly, germination of the mutualistic basidiomycete mycorrhizal fungus Piriformospora indica was affected only at concentrations beyond 50 μM. These results suggest that antifungal peptides from insects are a valuable source for crop plant improvements and their differential activities toward different phyla of fungi denote a capacity for insect peptides to be used as selective measures on specific plant diseases

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely