The landscape of antibiotic usage among COVID-19 patients in the early phase of pandemic: a Malaysian national perspective

Background During the early phase of the COVID-19 pandemic, antibiotic usage among COVID-19 patients was noted to be high in many countries. The objective of this study was to determine the prevalence of antibiotic usage and factors affecting antibiotic usage among COVID-19 patients during the early phase of the COVID-19 pandemic in Malaysia. Methods This was a cross-sectional study that involved reviewing medical records of COVID-19 Malaysian patients aged 12 and above who were diagnosed with COVID-19 and received treatment in 18 COVID-19 hospitals from February to April 2020. A minimum sample of 375 patients was required. A binary logistic regression analysis was performed to determine factors associated with antibiotic usage. Variables with p < 0.05 were considered statistically significant. Results A total of 4043 cases were included for analysis. The majority of the patients (87.6%) were non-smokers, male (65.0%), and had at least one comorbidity (37.0%). The median age was 35 years (IQR: 38). The prevalence of antibiotic usage was 17.1%, with 5.5% of them being prescribed with two or more types of antibiotics. The most frequent antibiotics prescribed were amoxicillin/clavulanic acid (37.8%), ceftriaxone (12.3%), piperacillin/tazobactam (13.3%), azithromycin (8.3%), and meropenem (7.0%). Male patients (adjusted OR 1.53), who had a comorbidity (adjusted OR 1.36), associated with more severe stage of COVID-19 (adjusted OR 6.50–37.06), out-of-normal range inflammatory blood parameters for neutrophils, lymphocytes, and C-reactive protein (adjusted OR 2.04–3.93), corticosteroid use (adjusted OR 3.05), and ICU/HDU admission (adjusted OR 2.73) had higher odds of antibiotic use. Conclusions The prevalence of antibiotic usage in the early phase of the COVID-19 pandemic was low, with amoxicillin/clavulanic acid as the most common antibiotic of choice. The study showed that clinicians rationalized antibiotic usage based on clinical assessment, supported by relevant laboratory parameters.

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events.Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19.Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic.Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant “rebound effect” by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003).Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

Acute Kidney Injury in Critical Care COVID-19 Patients on Invasive Mechanical Ventilation: The Potential Preventive Role of Dexamethasone

Background. A high incidence of acute kidney injury (AKI) has been reported in COVID-19 patients in critical care units and those undergoing invasive mechanical ventilation (IMV). The introduction of dexamethasone as treatment for severe COVID-19 has improved mortality, but its effects in other organs remain under study. Methods. In this prospective observational cohort study, we evaluated the incidence of AKI in critically ill COVID-19 patients undergoing mechanical ventilation, and the association of dexamethasone treatment with the incidence, severity, and outcomes of AKI. The association between dexamethasone treatment and AKI was evaluated by multivariate logistic regression. The association of the combination of dexamethasone treatment and AKI on mortality was evaluated by Cox-regression analysis. Results. We included 552 patients. AKI was diagnosed in 311 (56%), of which 196 (63%) corresponded to severe (stage 2 or 3) AKI, and 46 (14.8%) received renal replacement therapy (RRT). Two hundred and sixty-seven (48%) patients were treated with dexamethasone. This treatment was associated to lower incidence of AKI (OR 0.34, 95%CI 0.22-0.52, p<0.001) after adjusting for age, body mass index, laboratory parameters, SOFA score, and vasopressor use. Dexamethasone treatment significantly reduced mortality in patients with severe AKI (HR 0.63, 95%CI 0.41-0.96, p=0.032). Conclusions. The incidence of AKI is high in COVID-19 patients under IMV. Dexamethasone treatment is associated with a lower incidence of AKI and a lower mortality in the group with severe AKI.

Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101

Rice is a staple food source for most countries in the world, including Indonesia. The problem of rice disease is a problem that is quite crucial and is experienced by many farmers. Approximately 200,000 - 300,000 tons per year the amount of rice attacked by pests in Indonesia. Considerable losses are caused by late-diagnosed rice plant diseases that reach a severe stage and cause crop failure. The limited number of Agricultural Extension Officers (PPL) and the Lack of information about disease and proper treatment are some of the causes of delays in handling rice diseases. Therefore, with the development of information technology and computers, it is possible to identify diseases by utilizing Artificial Intelligence, one of which is by using recognition methods based on image processing and pattern recognition technology. The purpose of this research is to create a Machine Learning model by applying the model architecture from Resnet101 combined with the model architecture from the author. The model proposed in this study produces an accuracy of 98.68%.

Acute kidney injury after cardiac surgery in children

<p>Acute kidney injury (AKI) after cardiac surgery in children remains a common clinical concern. The approaches developed recently and applied in clinical practice have sufficiently helped in clarifying the epidemiology, risk factors and pathophysiology of AKI in paediatric cardiac surgery. Pediatric Risk, Injury, Failure, Loss, End-Stage Renal Disease criteria (pRIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO), which are based on changes in serum creatinine levels and urine output rate, enable the identification and ranking of AKI according to severity. However, the diagnostic strategies for AKI have developed beyond creatinine levels and recommend the use of markers of renal tissue damage. Currently, two markers, neutrophil gelatinase-associated lipocalin and TIMP-2/IGFBP-7 (tissue inhibitor of metalloproteinase 2 and protein that binds insulin-like growth factor-7), can be used for the early diagnosis of AKI in paediatric cardiac surgery.<br />Various risk factors, both renal and extrarenal, can predict AKI after cardiac surgery, among which age, the duration of cardiopulmonary bypass and the need for mechanical ventilation and inotropic support before surgery, are the most significant. Strategies for addressing modifiable risk factors (maintaining appropriate perfusion pressure during cardiopulmonary bypass and avoiding nephrotoxic drugs and fluid overload) will reduce the risk of developing AKI. There has been a significant increase in survival rates due to the introduction of ultrafiltration techniques and the early initiation of renal replacement therapy in the postoperative period.<br />The purpose of this review is to analyse the current literature data on AKI in paediatric cardiac surgery. The review results demonstrate the differences in the incidence of AKI associated with cardiac surgery and the effectiveness of certain methods for prevention and treatment of this complication. Further comprehensive research on the issue of AKI in children, creation of medical electronic databases on patients, minimisation of the influence of possible risk factors and timely prevention and treatment of complications would prevent the development of AKI and reduce the possibility of complication progression to a more severe stage.</p><p>Received 12 April 2021. Revised 24 June 2021. Accepted 25 June 2021.</p><p><strong>Funding:</strong> The study did not have sponsorship.</p><p><strong>Conflict of interest: </strong>Authors declare no conflict of interest.</p><p><strong>Contribution of the authors:</strong> The authors contributed equally to this article.</p>

Evaluation of Tooth Wear and its Causative Risk Factors Amongst Patients Attending Dental Hospital of Karachi

OBJECTIVE: Tooth wear is a term defined as the loss of dental hard tissue in a damaged tooth if there is no existing dental caries or trauma. Tooth wear rarely exists alone and is observed clinically and experimentally in combination. Excessive tooth wear leads to hypersensitivity and exposed dentin. Tooth wear can be classified as attrition, erosion, abrasion. The frequency of normal tooth preservation is greater than ever, thus a better prevalence of tooth wear is experiential in the population. Therefore, the objective of the present cross-sectional study was to evaluate tooth wear and its causative risk factors amongst patients attending the Dental Hospital of Karachi. METHODOLOGY: The current study was a cross-sectional study conducted on adult patients recruited from the Out-Patient Department of Oral Diagnosis from May 2018 - December 2018. A consecutive sampling method was used and 250 adult patients aged 18-45 years were included. Tooth wear was assessed by using Smith and Knight Tooth Wear Index (TWI index).Data was entered and analyzed by using SPSS, frequency, percentages were calculated, and a chi-square test was performed to find the association between gender and risk factors. RESULTS: The subject populations of 250 were assessed. Out of which 178 were male (71.2%) and 72 (28.8%) were female with age ranging between 18 to 48 years old. In this study, 92.4% of them were suffering from tooth wear and only 31.2% have extended to the severe stage of the tooth surface loss. CONCLUSION: The present study concluded that there is an association between tooth wear and its risk factors. KEYWORDS: Tooth Wear; Dental Wear; Tooth Wear Indices; Sensitivity and Specificity; Risk Factors.

Morphological Changes in Blood Cells as Indicators for Disease Progression in COVID 19

A novel highly pathogenic human corona virus (COVID19) has been recently recognised in Wuhan, China as the cause of corona disease outbreak. It has rapidly spread from China to various countries across the world evolving as a pandemic. In our study we have categorized the covid positive patients into mild, moderate and severe based on the clinical criteria suggested by WHO. The coagulation parameters of the patients were analysed and documented. A peripheral smear was made for every patient and the morphological changes in blood cells were documented. The peripheral smear findings were then correlated with the disease stage and coagulation parameters. There were significant differences in the total WBC count and the differential WBC count between stages 1 &2 and stages 1 & 3 (p<0.005). Leucocytosis, neutrophilia and toxic changes in neutrophils were seen in severe stage of the disease and in covid coagulopathy suggesting these are important indicators of disease severity. Schistocytes an important finding in any other coagulopathy was not present in covid associated coagulopathy. Activated lymphocytes was found to be the most common morphological presentation seen in all covid patients irrespective of the disease stage whereas plasmacytoid lymphocytes was an important finding in severe stage disease. Monocyte cytoplasmic vacuoles, large/giant platelets were other morphological findings observed but these findings did not have any significant correlation with disease stage. Since follow up smears of the same patient were not analysed during disease progression and also post recovery, additional research in this field will provide further insights.

Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures

Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.

Exploring Caregivers’ Clothing Choices for Individuals With Severe Alzheimer's Disease: An Application of the Theories of Self-Complexity and Choosing for Others

The number of older Americans as well as those living with Alzheimer's is rapidly growing. Alzheimer's dementia is a disease that causes problems with memory, thinking, and behavior. The role of caregivers is important, as they are the individuals who assist those with Alzheimer's in completing not just medical tasks, but fundamental activities of daily living, such as selecting garments to wear and getting dressed. The purpose of this study was to understand how caregivers make such choices. Interviews with twelve caregivers of individuals with severe stage Alzheimer's were conducted in nursing homes in the United States. Four themes emerged: The Role of Proxy, Routine Selections, Gift-Giving for Loved Ones, and Triangular Relationships. In all cases, the recipient's preferences were important to caregivers’ choices. Further research on the outcomes of making choices for others is needed.

A 26-Year-Old Woman with Bronchial Asthma and COVID -19 Treated by Stand Alone Siddha Medicine: A Case Report

Background: Corona virus disease 2019 (COVID-19) is a public health emergency of international concern. The global population lacks immunity to COVID-19 and is generally susceptible. Underlying conditions, especially chronic respiratory diseases, may affect progression, treatment and prognosis of COVID-19. The majority of people who exposed to COVID-19 suffer only mild respiratory symptoms like cough, cold, difficulty in breathing, etc and these symptoms were correlated with Kaba Suram in the Siddha literature. Case Summary: Siddha Clinical Research Unit, New Delhi (CCRS), Ministry of AYUSH, Govt. of India had reported a patient with confirmed COVID-19 by RT-PCR with bronchial asthma as a co-morbid condition. Recovery time from disease onset to negative test for COVID-19 was 19 days. Conclusion: Since the patient residing in Dwaraka, New Delhi has bronchial asthma as a co-morbid condition, both air pollution and the winter season are likely to increase the severity of the disease. But it was observed that the patient’s condition did not deteriorate, so it could be presumed that the management of COVID-19 with the given Siddha internal medicines and external therapies as a standalone treatment ceased the progress of the disease to a severe stage. Key words: Bronchial Asthma, Corona virus, COVID-19, Kaba Suram, Siddha Medicine.