A histo-morphological characteristics of gonads in Mudskipper, Periophthalmus waltoni Koumans, 1941 from Helleh estuary, Southwestern Iran

In this study, the morphological and histological studies of male and female gonads in mudskipper, Periophthalmus waltoni Koumans, 1941 from Helleh estuary (Bushehr Province, southwestern Iran) were performed to determine its gonadal development stages and histo-morphological characteristics. Sampling was done from April 2010 to March 2011 and a total of 81 individuals were collected by hand net. The gonads of specimens were removed, their sexes determined and then fixed in 10% formalin solution after checking their morphology and measuring their weights, lengths and widths. Six stages of gonadal development in females and four stages in males were determined based on macroscopic and microscopic observations and reproductive indices. In female, increasing of the ovary size is occurred because of the accumulation of yolk materials in oocytes, and in the last stages, little folding in ovary was observed. Formation of zona radiata and yolk granules in the third stage, and increasing thickness of this layer and yolk granules were observed from stage three to stage six. In male, gradual developments of the sperm cells were observed from stage one onward

Epidemiological Study of Intestinal Parasites in Referred Individuals to the Medical Centers’ Laboratories of Haji-Abad City, Hormozgan Province, Iran, 2015

Background:intestinal parasitic infection is one of the most prevalent health problems in developing countries. This study was conducted to determine the prevalence of intestinal parasitic infection and its correlation with socio-demographic parameters in Haji-abad, 2015.MaterialsandMethods:This cross-sectional descriptive study was conducted on 635 samples. After completing questionnaires, stool samples were assessed macroscopically, and microscopically using direct slide smear with saline and lugol, formalin-ether concentration, Ziehl-Neelsen staining to track Cryptosporidium species and Trichrome staining for the samples suspected to amoeba and other indeterminate cases. PCR using specific primers was conducted for Entamoebahistolytica/E. dispar suspected samples. The results were analyzed using SPSSver.16 software.Results:Of total 635 samples, 198 cases (31.2%) were infected by at least one intestinal parasite. The most common parasites in this area were: Blastocystis sp. (105, 16.5%), Endolimax nana (43, 6.8%), Entamoeba coli (32, 5.0%), Giardia lamblia (31, 4.9%), and Iodamoeba butschlii (11, 1.7%). Enterobius vermicularis (1, 0.2%) was the only detected helminthic infection. Regarding socio-demographic variables, age, residence, sampling month, and job showed a significant correlation with IPIs (p-value=0.031, 0.019, 0.014, 0.012; respectively). None of nine microscopically suspected E. histolytica/E. dispar cases were confirmed by molecular investigations (PCR method) and were considered as E. coli.Conclusion:In agreement with previous studies, helminthes infections show a dramatic decline compare to protozoa in this study. The relatively high incidence of intestinal protozoan infections in studies performed in Iran supports strategies for pre­venting the transmission and expansion of these parasites as a priority

A histo-morphological characteristics of gonads in Mudskipper, Periophthalmus waltoni Koumans, 1941 from Helleh estuary, Southwestern Iran

In this study, the morphological and histological studies of male and female gonads in mudskipper, Periophthalmus waltoni Koumans, 1941 from Helleh estuary (Bushehr Province, southwestern Iran) were performed to determine its gonadal development stages and histo-morphological characteristics. Sampling was done from April 2010 to March 2011 and a total of 81 individuals were collected by hand net. The gonads of specimens were removed, their sexes determined and then fixed in 10% formalin solution after checking their morphology and measuring their weights, lengths and widths. Six stages of gonadal development in females and four stages in males were determined based on macroscopic and microscopic observations and reproductive indices. In female, increasing of the ovary size is occurred because of the accumulation of yolk materials in oocytes, and in the last stages, little folding in ovary was observed. Formation of zona radiata and yolk granules in the third stage, and increasing thickness of this layer and yolk granules were observed from stage three to stage six. In male, gradual developments of the sperm cells were observed from stage one onward

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.Validerad;2022;Nivå 2;2022-02-18 (sofila);Funder: University of Kurdistan, Iran (grant no. 11-99-4469)</p

Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset

Background Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi‐institutional cohort of patients with COVID‐19 using a DL‐based model. Purpose This study aimed to evaluate the performance of deep privacy‐preserving federated learning (DPFL) in predicting COVID‐19 outcomes using chest CT images. Methods After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold‐out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold‐out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. Results The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79–0.85) and (95% CI: 0.77–0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models ( p ‐value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. Conclusion The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi‐institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.</p

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p