Longitudinal changes of laboratory measurements after discharged from hospital in 268 COVID-19 pneumonia patients

Mental Health of COVID-19 Survivors at 6 and 12 Months Postdiagnosis: A Cohort Study

OBJECTIVE

As COVID-19 persists around the world, it is necessary to explore the long-term mental health effects in COVID-19 survivors. In this study, we investigated the mental health outcomes of survivors of COVID-19 at 6 and 12 months postdiagnosis.

METHODS

Posttraumatic stress disorder (PTSD checklist for the DSM-5, PCL-5), depression (PHQ-9), anxiety (Generalized Anxiety Disorder Scale, GAD-7), resilience (Connor-Davidson Resilience Scale, CD-RISC-10), perceived social support (PSSS), personality traits (Chinese Big Five Personality Inventory-15, CBF-PI-15), and sociodemographic information were examined among 511 survivors of COVID-19 (48.1%, females; M _age = 56.23 years at first assessment) at 6 and 12 months postdiagnosis. The data were analyzed with Wilcoxon signed rank tests and multivariable logistic regression models.

RESULTS

The prevalence of anxiety, depression, and posttraumatic stress disorder (PTSD) at 6 and 12 months after diagnosis was 13.31% and 6.26%; 20.35% and 11.94%; and 13.11% and 6.07%, respectively. The risk factors for all symptoms were as follows: higher neuroticism; lower openness, extraversion, agreeableness, and resilience; greater life disruptions due to COVID-19; poorer living standards; and increased symptoms of PTSD or depression at 6 months postdiagnosis.

CONCLUSION

The mental health of COVID-19 survivors improved between 6 and 12 months postdiagnosis. Mental health workers should pay long-term attention to this group, especially to survivors with risk factors.

Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

BACKGROUND

Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a variety of symptoms such as fatigue, fever, tastelessness, dry cough, etc. Some other symptoms may also be manifested by radiographic visual identification. Therefore, Chest X-Rays (CXR) play a key role in the diagnosis of COVID-19.

METHODS

In this study, we use Chest X-Rays images to develop a computer-aided diagnosis (CAD) of the disease. These images are used to train two deep networks, the Convolution Neural Network (CNN), and the Long Short-Term Memory Network (LSTM) which is an artificial Recurrent Neural Network (RNN). The proposed study involves three phases. First, the CNN model is trained on raw CXR images. Next, it is trained on pre-processed CXR images and finally enhanced CXR images are used for deep network CNN training. Geometric transformations, color transformations, image enhancement, and noise injection techniques are used for augmentation. From augmentation, we get 3,220 augmented CXRs as training datasets. In the final phase, CNN is used to extract the features of CXR imagery that are fed to the LSTM model. The performance of the four trained models is evaluated by the evaluation techniques of different models, including accuracy, specificity, sensitivity, false-positive rate, and receiver operating characteristic (ROC) curve.

RESULTS

We compare our results with other benchmark CNN models. Our proposed CNN-LSTM model gives superior accuracy (99.02%) than the other state-of-the-art models. Our method to get improved input, helped the CNN model to produce a very high true positive rate (TPR 1) and no false-negative result whereas false negative was a major problem while using Raw CXR images.

CONCLUSIONS

We conclude after performing different experiments that some image pre-processing and augmentation, remarkably improves the results of CNN-based models. It will help a better early detection of the disease that will eventually reduce the mortality rate of COVID.