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Government should improve the healthcare system suitable for the older adult population as soon as possible. The government of western China should pay more attention to the introduction of professional medical talents and the configuration of precision medical equipment to improve the health system in western China.

Government should improve the healthcare system suitable for the older adult population as soon as possible. The government of western China should pay more attention to the introduction of professional medical talents and the configuration of precision medical equipment to improve the health system in western China.

The process of learning begins in childhood and accurate vision can greatly affects a child's learning capacity. It is documented that visual impairment in children can have a significant impact on their performance at school as well as their social interaction and development.

This research aimed to study the impact of refractive corrections on the academic performance of high school children in Lahore.

A total of 2,000 students with equal distribution of gender, public, private school, and locality were included in the study. All students were screened for defective vision. The academic performance before and after corrections was recorded on the prescribed proforma.

The prevalence of refractive error was high among the public high schools 244 (59.2%) as compared to the private schools 168 (40.8%). The area-based prevalence was higher among the students in urban settings 255 (62%) while in rural it was 157 (38%). It was found that in the public sector, the average score of academic results before the intervention was 56.39 ± 13.24 which was increased to 60.27 ± 14.94 after the intervention while in the private sector, before the intervention, the average score was 63.53 ± 17.50 which was improved to 67.12 ± 18.48. It was found to be statistically significant at

-value < 0.05.

A significant impact was observed in the average academic scores of the results after refractive corrections.

A significant impact was observed in the average academic scores of the results after refractive corrections.

Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians.

The method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with "jieba" word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. selleck chemicals llc The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information.

The precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and

1 scores were 0.6, 0.81, 0.88, respectively.

Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.An umbrella review of meta-analyses was performed to summarize the evidence of associations between alcohol consumption and health outcomes and to assess its credibility. Meta-analyses of prospective cohort studies reporting the associations of alcohol consumption with health outcomes were identified. We recalculated the random-effects summary effect size and 95% confidence interval, heterogeneity, and small-study effect for each meta-analysis and graded the evidence. Fifty-nine publications reporting 224 meta-analyses of prospective cohort studies with 140 unique health outcomes were included, in which there were 49 beneficial associations and 25 harmful associations with nominally statistically significant summary results. But quality of evidence was rated high only for seven beneficial associations (renal cell carcinoma risk, dementia risk, colorectal cancer mortality, and all-cause mortality in patients with hypertension for low alcohol consumption; renal cell carcinoma risk, cardiovascular disease (CVD) risk in patients with hypertension and all-cause mortality in patients with hypertension for moderate consumption) and four harmful associations (cutaneous basal cell carcinoma risk for low alcohol consumption; cutaneous basal cell carcinoma risk and cutaneous squamous cell carcinoma risk for moderate alcohol consumption; hemorrhagic stroke risk for high alcohol consumption). In this umbrella review, only 11 health outcomes (5 in low alcohol consumption, 5 in moderate alcohol consumption and 1 in high alcohol consumption) with statistically significant showed high quality of epidemiologic evidence. More robust and larger prospective studies are needed to verify our results.

Psychological injuries in social work are on the rise in complex modern society. Some individuals are incurring both physical and psychological injuries. Often, psychological injuries are more miserable than physical injuries. To combat the psychological injury suffered by individuals involved in social work, authorities should mobilize support via social media and raise funds by this and other feasible means to cover the cost of care for these individuals. This study focuses on social media support and funding assistance that could play useful roles in helping to treat psychological injuries among social workers and their clients in China.

A scoping review of academic and gray literature was undertaken to identify the different injuries involved in social work. Semi-structured interviews were carried out with 7 experts, including social workers, social media professionals, and social fund directors. Empirical studies on psychological injuries in social work provided examples in support of the policy advorap in China.

Psychological injury is greatly influenced by social bias and discrimination. According to cases and actions are taken to mitigate the harm done, supportive social media strategies could greatly diminish the psychological injuries to social workers and their clients and help them avoid much suffering. This study finds that funding organizations could provide a new treatment mechanism-social media marketing strategies and functional activities-to help a large number of individuals with psychological injuries out of the disease trap in China.Given the rapidly changing political rhetoric and policies concerning immigration, and the likely impact of this rhetoric on immigrants' adjustment, it is essential to understand the experiences of recently arrived immigrant individuals and families. This article describes methods to recruit and retain recently arrived Hispanic families in longitudinal research and clinical practice. Barriers to continued engagement with recent-immigrant families include residential mobility, wariness toward authority figures (including researchers and practitioners), and unpredictable work schedules. These barriers can lead to challenges related to recruitment/engagement, logistics, establishing trust, and retention. This article describes decisions made, experiences, and lessons learned in a longitudinal study of Hispanic families in two cities. We also provide implications for clinical practice.Since December 2019, the pandemic COVID-19 has been connected to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early identification and diagnosis are essential goals for health practitioners because early symptoms correlate with those of other common illnesses including the common cold and flu. RT-PCR is frequently used to identify SARS-CoV-2 viral infection. Although this procedure can take up to 2 days to complete and sequential monitoring may be essential to figure out the potential of false-negative findings, RT-PCR test kits are apparently in low availability, highlighting the urgent need for more efficient methods of diagnosing COVID-19 patients. Artificial intelligence (AI)-based healthcare models are more effective at diagnosing and controlling large groups of people. Hence, this paper proposes a novel AI-enabled SARS detection framework. Here, the input CT images are collected and preprocessed using a block-matching filter and histogram equalization (HE). Segmentation is performed using Compact Entropy Rate Superpixel (CERS) technique. Features of segmented output are extracted using Histogram of Gradient (HOG). Feature selection is done using Principal Component Analysis (PCA). The suggested Random Sigmoidal Artificial Neural Networks (RS-ANN) based classification approach effectively diagnoses the existence of the disease. The performance of the suggested Artificial intelligence model is analyzed and related to existing approaches. The suggested AI system may help identify COVID-19 patients more quickly than conventional approaches.In this contemporary world, the words data and sustainability play a crucial role in determining the financial budgets of small and medium-sized enterprises (SMEs). Usually, it is stated that the survival of small and medium-sized enterprises (SMEs) is directly proportional to the growth and sustainability factor of the nation. The economic sustainability of a nation is dependent on appropriate functioning of SMEs. Any kind of direct impact on the working of SMEs will have its impact on the whole economy of a nation. There are different factors such as lack of financial capacity, low market demands, restrictions with regard to the capital, and barriers in the supply chain that affect the sustainability of SMEs worldwide. Nevertheless, small, and medium sized enterprises around the world are greatly investing on skills, innovation, and other capital related resources to mark up the demands of the external market. The main objective of this study is to examine the financial budgets of technology-based SMEs from the perspective of sustainability and big data. For this, the study collects data through a questionnaire from 1,800 Small and Medium Sized Enterprises. Based on a detailed and careful examination of the data, only 1,400 of the responses received were considered valid (79.75%). To test the hypothesis stated, the study employs structural equation modeling. This will help the researcher to examine the direct effect of financial budget and technology adaption of SMEs from the perspective of sustainability and big data. Results of the study stated that SMEs sustainability and big data are directly and positively related to the financial budget planning of technology-based SMEs. The study also found that big data plays an important role in the businesses, specifically for their own growth.