Effect of sojourn time distributions on the early dynamics of COVID-19 outbreak

Compartmental models are commonly used in practice to investigate the dynamical response of infectious diseases such as the COVID-19 outbreak. Such models generally assume exponentially distributed latency and infectiousness periods. However, the exponential distribution assumption fails when the sojourn times are expected to distribute around their means. This study aims to derive a novel S (Susceptible)-E (Exposed)-P (Presymptomatic)-A (Asymptomatic)-D (Symptomatic)-C (Reported) model with arbitrarily distributed latency, presymptomatic infectiousness, asymptomatic infectiousness, and symptomatic infectiousness periods. The SEPADC model is represented by nonlinear Volterra integral equations that generalize ordinary differential equation-based models. Our primary aim is the derivation of a general relation between intrinsic growth rate r and basic reproduction number R 0 with the help of the well-known Lotka-Euler equation. The resulting r - R 0 equation includes separate roles of various stages of the infection and their sojourn time distributions. We show that R 0 estimates are considerably affected by the choice of the sojourn time distributions for relatively higher values of r. The well-known exponential distribution assumption has led to the underestimation of R 0 values for most of the countries. Exponential and delta-distributed sojourn times have been shown to yield lower and upper bounds of the R 0 values depending on the r values. In quantitative experiments, R 0 values of 152 countries around the world were estimated through our novel formulae utilizing the parameter values and sojourn time distributions of the COVID-19 pandemic. The global convergence, R 0 = 4.58 , has been estimated through our novel formulation. Additionally, we have shown that increasing the shape parameter of the Erlang distributed sojourn times increases the skewness of the epidemic curves in entire dynamics.

Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy

Human Immunodeficiency Virus (HIV) is one of the most common chronic infectious diseases in humans. Extending the expected lifetime of patients depends on the use of optimal antiretroviral therapies. Emergence of the drug-resistant strains can reduce the effectiveness of treatments and lead to Acquired Immunodeficiency Syndrome (AIDS), even with antiretroviral therapy. Investigating the genotype-phenotype relationship is a crucial process for optimizing the therapy protocols of the patients. Here, a mathematical modelling framework is proposed to address the impact of existing mutations, timing of initiation, and adherence levels of nucleotide reverse transcriptase inhibitors (NRTIs) on the evolutionary dynamics of the virus strains. For the first time, the existing Stanford HIV drug resistance data have been combined with a multi-strain within-host ordinary differential equation (ODE) model to track the dynamics of the most common NRTI-resistant strains. Overall, the D4T-3TC, D4T-AZT and TDF-D4T drug combinations have been shown to provide higher success rates in preventing treatment failure and further drug resistance. The results are in line with the genotype-phenotype data and pharmacokinetic parameters of the NRTI inhibitors. Moreover, we show that the undetectable mutant strains at the diagnosis have a significant effect on the success/failure rates of the NRTI treatments. Predictions on undetectable strains through our multi-strain within-host model yielded the possible role of viral evolution on the treatment outcomes. It has been recognized that the improvement of multi-scale models can contribute to the understanding of the evolutionary dynamics, and treatment options, and potentially increase the reliability of genotype-phenotype models.

Prediction of HIV-1 protease resistance using genotypic, phenotypic, and molecular information with artificial neural networks

Drug resistance is a primary barrier to effective treatments of HIV/AIDS. Calculating quantitative relations between genotype and phenotype observations for each inhibitor with cell-based assays requires time and money-consuming experiments. Machine learning models are good options for tackling these problems by generalizing the available data with suitable linear or nonlinear mappings. The main aim of this study is to construct drug isolate fold (DIF) change-based artificial neural network (ANN) models for estimating the resistance potential of molecules inhibiting the HIV-1 protease (PR) enzyme. Throughout the study, seven of eight protease inhibitors (PIs) have been included in the training set and the remaining ones in the test set. We have obtained 11,803 genotype-phenotype data points for eight PIs from Stanford HIV drug resistance database. Using the leave-one-out (LVO) procedure, eight ANN models have been produced to measure the learning capacity of models from the descriptors of the inhibitors. Mean R² value of eight ANN models for unseen inhibitors is 0.716, and the 95% confidence interval (CI) is [0.592-0.840]. Predicting the fold change resistance for hundreds of isolates allowed a robust comparison of drug pairs. These eight models have predicted the drug resistance tendencies of each inhibitor pair with the mean 2D correlation coefficient of 0.933 and 95% CI [0.930-0.938]. A classification problem has been created to predict the ordered relationship of the PIs, and the mean accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) values are calculated as 0.954, 0.791, 0.791, and 0.688, respectively. Furthermore, we have created an external test dataset consisting of 51 unique known HIV-1 PR inhibitors and 87 genotype-phenotype relations. Our developed ANN model has accuracy and area under the curve (AUC) values of 0.749 and 0.818 to predict the ordered relationships of molecules on the same strain for the external dataset. The currently derived ANN models can accurately predict the drug resistance tendencies of PI pairs. This observation could help test new inhibitors with various isolates.

The Evaluation of 13 Patients with Intrathoracic Extrapulmonary Hydatidosis

Cases of intrathoracic extrapulmonary hydatid cysts are very rare. We identified 13 patients with intrathoracic extra-pulmonary hydatid cysts in our clinic over 12 years. Four patients had extra-pulmonary cysts only; nine patients had both intrapulmonary and extrapulmonary cysts. Cysts were identified in the pleural space, extrapleural region, diaphragm and chest wall. Thoracotomy was used in all patients, and extrapulmonary lesions were removed by cyst extirpation from surrounding tissue or by pericystectomy. In one patient with chest wall involvement, partial rib resections were performed because of rib destruction. In two patients with liver cysts passing through the diaphragm to the thorax, the diaphragm was cut, cysts on the liver roof were removed and then the diaphragm was repaired. There was no mortality, morbidity, or disease recurrence during the post-operative period in any of the 13 patients. We conclude that these rare cases give a new insight into hydatid cyst pathophysiology.

Clinical experience of surgical therapy in 207 patients with thoracic hydatidosis over a 12-year-period

PRINCIPLES

Hydatid disease is the most severe helminthic zoonosis, with a major medical, social, and economic impact in Turkey. The aim of this study was to evaluate retrospectively 207 patients diagnosed with hydatid cyst and treated surgically in our department between January 1990 and December 2001.

METHODS

Hundred and ninety three patients were male and 14 female. They ranged in age from 19 to 72 years (mean 25.3 years). The most common presenting symptoms were cough, expectoration and chest pain. The surgical approach was thoracotomy in 198 patients, bilateral staged thoracotomies in 5 patients, median sternotomy in one patient and video-assisted thoracic surgery in 3 patients.

RESULTS

Hundred and thirty eight of the 265 intrapulmonary cystic lesions were found in the right lung and 127 in the left lung. Intrathoracic extrapulmonary cystic lesions were detected in 13 patients. 38 patients also had cystic lesions in the liver. Conservative surgical procedures were adopted except for small wedge resections in 8 patients, segmentectomy in 4 patients and lobectomy in one. Operative and postoperative mortality was nil. Albendazole treatment was given to patients who had multiple intrathoracic cysts or additional hepatic cysts after 1994.

CONCLUSIONS

Our preferred surgical techniques for removal of cysts were conservative surgical procedures such as enucleation of cysts or removal by cystotomy. Radical procedures such as pneumonectomy, lobectomy and segmentectomy should be avoided as far as possible.

Clinical experience of surgical therapy in 207 patients with thoracic hydatidosis over a 12-year-period

PRINCIPLES

Hydatid disease is the most severe helminthic zoonosis, with a major medical, social, and economic impact in Turkey. The aim of this study was to evaluate retrospectively 207 patients diagnosed with hydatid cyst and treated surgically in our department between January 1990 and December 2001.

METHODS

Hundred and ninety three patients were male and 14 female. They ranged in age from 19 to 72 years (mean 25.3 years). The most common presenting symptoms were cough, expectoration and chest pain. The surgical approach was thoracotomy in 198 patients, bilateral staged thoracotomies in 5 patients, median sternotomy in one patient and video-assisted thoracic surgery in 3 patients.

RESULTS

Hundred and thirty eight of the 265 intrapulmonary cystic lesions were found in the right lung and 127 in the left lung. Intrathoracic extrapulmonary cystic lesions were detected in 13 patients. 38 patients also had cystic lesions in the liver. Conservative surgical procedures were adopted except for small wedge resections in 8 patients, segmentectomy in 4 patients and lobectomy in one. Operative and postoperative mortality was nil. Albendazole treatment was given to patients who had multiple intrathoracic cysts or additional hepatic cysts after 1994.

CONCLUSIONS

Our preferred surgical techniques for removal of cysts were conservative surgical procedures such as enucleation of cysts or removal by cystotomy. Radical procedures such as pneumonectomy, lobectomy and segmentectomy should be avoided as far as possible.