Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model's ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

Clinico-epidemiological Characteristics of Asymptomatic and Symptomatic COVID-19-Positive Patients in Bangladesh.

As of August 15, 2020, Bangladesh lost 3591 lives since the first Coronavirus disease 2019 (COVID-19) case announced on March 8. The objective of the study was to report the clinical manifestation of both symptomatic and asymptomatic COVID-19-positive patients. An online-based cross-sectional survey was conducted for initial recruitment of participants with subsequent telephone interview by the three trained physicians in 237 adults with confirmed COVID-19 infection in Bangladesh. The study period was 27 April to 26th May 2020. Consent was ensured before commencing the interview. Collected data were entered in a pre-designed case record form and subsequently analyzed by SPSS 20.0. The mean±SD age at presentation was 41.59±13.73 years and most of the cases were male (73.0%). A total of 90.29% of patients reside in urban areas. Among the positive cases, 13.1% (n=31) were asymptomatic. Asymptomatic cases were significantly more common in households with 2 to 4 members (p=0.008). Both symptomatic and asymptomatic patients shared similar ages of presentation (p=0.23), gender differences (p=0.30) and co-morbidities (p=0.11). Only 5.3% of patients received ICU care during their treatment. The most frequent presentation was fever (88.3%), followed by cough (69.9%), chest pain (34.5%), body ache (31.1%), and sore throat (30.1%). Thirty-nine percent (n=92) of the patients had co-morbidities, with diabetes and hypertension being the most frequently observed. There has been an upsurge in COVID-19 cases in Bangladesh. Patients were mostly middle-aged and male. Typical presentations were fever and cough. Maintenance of social distancing and increased testing are required to meet the current public health challenge.

Impact of Comorbidities on Clinical Outcome of Patients with COVID-19: Evidence From a Single-center in Bangladesh

Background: COVID-19 patients with preexisting comorbidities are at increased risk of exacerbated symptoms. Objectives: The current study aimed to firstly assess the impact of predisposed comorbidities on the severity of COVID-19, and secondly investigating the associated clinical outcome of patients with COVID-19 infection in Bangladesh. Methods: In this single-center retrospective study, the medical data of 157 hospitalized COVID-19 patients, including their preexisting comorbidities, from April 30, 2020, to June 15, 2020, are analyzed. Patients' clinical outcomes in moderate-to-critical COVID-19 infections need for Intensive Care Unit (ICU) and mechanical ventilation support, and mortality were evaluated, with emphasis on predisposed chronic diseases. Results: Approximately 40.1 and 7.6% of patients (n = 157) presented severe and critical COVID-19 symptoms, respectively (P = 0.001). The most common comorbidity was diabeties (24.8%), followed by hypertension (23.2). Patients with one or two comorbidities did not present critical symptoms. Most of the critical cases had at least five comorbidities compared to those with 3 or 4 comorbidities (33.3% versus 8.3%;P = 0.038). The highest incidence of critical COVID-19 (41.7%) was among those with 7 comorbidities. Compared to patients with 4 or fewer comorbidities, patients with 5 (n = 15), 6 (n = 4), and 7 (n = 7) comorbidities were more hospitalized at ICU (above 70%, P = 0.025) and had a higher need for intubation support (above 60%, P = 0.038), and presented higher 30-day mortality (6.7, 25, and 28.6%, respectively;P = 0.002), which can be attributed to the declined clinical outcome of patients with 5 or more comorbidities in moderate-to-critical COVID-19 infection. Conclusions: This study demonstrated a positive association between the severity of COVID-19 and the number of predisposed comorbidities, which leads to poor clinical outcomes.

Mental Health of the COVID-19 Patients in Bangladesh.

The mental health aspect of coronavirus disease-19 (COVID-19) patients in Bangladesh has remained less focused and has not been addressed properly. The objective of the study was to assess the levels of anxiety and depression in COVID-19 patients. We adopted a mixed online and telephone-based survey using Google Forms. Recruitment was performed through a snowball sampling approach. The Google Form was initially circulated in Facebook to identify interested participants. Then, three trained physicians interviewed the online responders over telephone for a period spanning from April 2020 to June 2020. Two well-known questionnaires, the Generalized Anxiety Disorder 7-item (GAD-7) scale and the Patient Health Questionnaire (PHQ-9), were used for the assessment of anxiety and depression, respectively. Here, the severity of anxiety was classified with the standard thresholds: minimal or none (0-4), mild (5-9), moderate (10-14) and severe (>15) for the GAD-7. Depression severity score: 0-4 was considered as none or no depression, 5-9: mild, 10-14: moderate, 15-19: moderately severe, 20-27 was for severe depression. A total of 237 patients were finally analyzed. The mean age ±SD of the patients was 41.59±13.73 years. Most of them were male (73%) and lived in urban areas (90.29%). Half of the patients were unemployed, and 17.7% admitted loss of job due to lockdown. The overall prevalence of anxiety and depression was 55.7% and 87.3%, respectively. The mean GAD-7 score was 5.79±4.95, and the mean PHQ-9 score was 5.64±5.15. Among the depressive patients, 3% had minimal depression, 38.4% had mild depression, 32.1% had moderate depression, 11.8% had moderate depression, and 2.1% had a severe depression. Similarly, 37.1%, 10.5% and 8% had mild, moderate and severe levels of anxiety, respectively. Nearly half of the study population (47.7%) was suffering from both depression and anxiety. Living in urban area was an independent predictor for depression (OR 3.882; CI: 1.249-12.069). Considering the high comorbid burden, the mental health issues of these patients need to be addressed and reinforced to the existing health system on a priority basis.