Effect of acute lithium administration on penile erection: involvement of nitric oxide system

Background: Lithium has been the treatment of choice for bipolar disorder (BD) for many years. Although erectile dysfunction is a known adverse effect of this drug, the mechanism of action by which lithium affects erectile function is still unknown. Objective: The aim was to investigate the possible involvement of nitric oxide (NO) in modulatory effect of lithium on penile erection (PE). We further evaluated the possible role of Sildenafil in treatment of lithium-induced erectile dysfunction. Materials and Methods: Erectile function was determined using rat model of apomorphine-induced erections. For evaluating the effect of lithium on penile erection, rats received intraperitoneal injection of graded doses of lithium chloride 30 mins before subcutaneous injection of apomorphine. To determine the possible role of NO pathway, sub-effective dose of N (G)-nitro-L-arginine methyl ester (L-NAME), a nitric oxide synthase (NOS) inhibitor, was administered 15 min before administration of sub-effective dose of lithium chloride. In other separate experimental groups, sub- effective dose of the nitric oxide precursor, L-arginine, or Sildenafil was injected into the animals 15 min before administration of a potent dose of lithium. 30 min after administration of lithium chloride, animals were assessed in apomorphine test. Serum lithium levels were measured 30 min after administration of effective dose of lithium. Results: Lithium at 50 and 100 mg/kg significantly decreased number of PE (p<0.001), whereas at lower doses (5, 10 and 30 mg/kg) had no effect on apomorphine induced PE. The serum Li+ level of rats receiving 50 mg/kg lithium was 1±0.15 mmol/L which is in therapeutic range of lithium. The inhibitory effect of Lithium was blocked by administration of sub-effective dose of nitric oxide precursor L-arginine (100 mg/kg) (p<0.001) and sildenafil (3.5 mg/kg) (p<0.001) whereas pretreatment with a low and sub-effective dose of L-NAME (10mg/kg) potentiated sub-effective dose of lithium, (p<0.001). Conclusion: These results suggest acute treatments with lithium cause erectile dysfunction in an in-vivo rat model. Furthermore it seems that the NO pathway might play role in erectile dysfunction associated with lithium treatment. Findings also suggest that Sildenafil may be effective in treatment of lithium-associated erectile dysfunction

Porównanie liczby niepodwiązanych gałęzi bocznych w przypadku pobierania tętnicy piersiowej wewnętrznej metodami endoskopową i tradycyjną

Background: In an effort to minimise access in cardiac surgery, endoscopic vessel harvesting has become more popular. Theendoscopic approach, however, allows for only the harvest of the mid to distal internal mammary artery (IMA), leaving themore proximal branches of the conduit available for collateral flow away from the coronary bed.Aim: To compare the number and anatomic variation of remaining side branches in thoracoscopic vs. conventional IMAharvesting.Methods: 199 fresh cadavers were randomly divided into two groups. Group A (n = 100) underwent endoscopic IMA harvesting. In Group B (n = 99), IMAs were harvested using an open conventional approach. In both groups during surgery, sidebranches of the IMA were isolated and identified.Results: The two groups were comparable with regard to mean age and age distribution, male sex (56% vs. 63%, respectively),cause of death and coronary risk factors including smoking, diabetes, dyslipidaemia and hypertension. 24 of 199 cadavers(12%) had a lateral costal branch. The left IMA arose from the third part of the subclavian artery in 6%, and from the thyrocervical trunk in 7% of the cadavers. There were significantly more unligated side branches in Group B compared toGroup A (14 branches vs. 3 branches, p &lt; 0.01). The first intercostal artery and lateral costal artery were found unligated in3% and 5% of cadavers in Group B, whereas no side branch remained unligated in Group A. There was no subclavian arteryor IMA injury in either group. Internal mammary vein was damaged in 2% of cadavers in Group B.Conclusions: Thoracoscopic left IMA harvesting is more accurate in finding and ligating the side branches of IMA.Wstęp: W związku z ograniczaniem dostępu kardiochirurgicznego coraz bardziej popularne staje się endoskopowe pobieranie naczyń krwionośnych. Metoda endoskopowa umożliwia jednak pobieranie tylko środkowego lub dalszego odcinka tętnicypiersiowej wewnętrznej (IMA), pozostawiając położone proksymalnie gałęzie konduitu dostępne dla krążenia obocznegopoza łożyskiem wieńcowym.Cel: Celem niniejszego badania było porównanie liczby i zmienności anatomicznej pozostałych gałęzi bocznych w przypadku pobierania IMA metodą torakoskopii i tradycyjną metodą otwartą.Metody: Na dwie grupy losowo podzielono 199 świeżych zwłok. W grupie A (n = 100) pobrano IMA endoskopowo, a w grupie B (n = 99) do pobrania IMA zastosowano tradycyjną metodę otwartą. W obu grupach podczas zabiegu wyizolowano i zidentyfikowano boczne gałęzie IMA.Wyniki: Grupy A i B były porównywalne pod względem średniej wieku i rozkładu płci (mężczyźni stanowili odpowiednio56% i 63%), przyczyny zgonu i czynników ryzyka wieńcowego, takich jak palenie tytoniu, cukrzyca, dyslipidemia i nadciśnienie tętnicze. W przypadku 24 spośród 199 zwłok (12%) stwierdzono występowanie bocznej gałęzi żebrowej. Lewa tętnica piersiowa wewnętrzna (LIMA) odchodziła od trzeciej części tętnicy podobojczykowej u 6% osób i od pnia tarczowo-szyjnego u 7% osób. W grupie B stwierdzono istotnie więcej niepodwiązanych gałęzi bocznych niż w grupie A (odpowiednio 14 gałęzii 3 gałęzie; p &lt; 0,01). Pierwsza tętnica międzyżebrowa i tętnica żebrowa boczna były niepodwiązane w przypadku 3%i 5% zwłok w grupie B, natomiast w grupie A nie stwierdzono ani jednego przypadku niepodwiązania tych naczyń. W żadnej z grup nie doszło do uszkodzenia tętnicy podobojczykowej ani piersiowej wewnętrznej. Żyła piersiowa wewnętrzna była uszkodzona w przypadku 2% zwłok w grupie B.Wnioski: Zastosowanie torakoskopii do pobierania LIMA ułatwia znalezienie i podwiązanie bocznych gałęzi tętnicy piersiowej wewnętrznej

PErspective and current status of Radiotherapy Service in IRan (PERSIR)-1 study: assessment of current external beam radiotherapy facilities, staff and techniques compared to the international guidelines

Abstract Background and purpose Radiotherapy (RT) is an essential treatment modality against cancer and becoming even more in demand due to the anticipated increase in cancer incidence. Due to the rapid development of RT technologies amid financial challenges, we aimed to assess the available RT facilities and the issues with achieving health equity based on current equipment compared to the previous reports from Iran. Materials and methods A survey arranged by the Iran Cancer Institute's Radiation Oncology Research Center (RORC) was sent to all of the country's radiotherapy centers in 2022. Four components were retrieved: the reimbursement type, equipment, human resources, and patient load. To calculate the radiotherapy utilization rate (RUR), the Lancet Commission was used. The findings were compared with the previous national data. Results Seventy-six active radiotherapy centers with 123 Linear accelerators (LINACs) were identified. The centers have been directed in three ways. 10 (20 LINACs), 36 (50 LINACs), and 30 centers (53 LINACs) were charity-, private-, and public-based, respectively. Four provinces had no centers. There was no active intraoperative radiotherapy machine despite its availability in 4 centers. One orthovoltage X-ray machine was active and 14 brachytherapy devices were treating patients. There were 344, 252, and 419 active radiation oncologists, medical physicists, and radiation therapy technologists, respectively. The ratio of LINAC and radiation oncologists to one million populations was 1.68 and 4.10, respectively. Since 2017, 35±5 radiation oncology residents have been trained each year. Conclusion There has been a notable growth in RT facilities since the previous reports and Iran's situation is currently acceptable among LMICs. However, there is an urgent need to improve the distribution of the RT infrastructure and provide more facilities that can deliver advanced techniques

Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging

Background: Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients. Results: A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior–posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and − 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was − 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose. Conclusion: The proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.</p

Treatment Response Prediction using MRI-based Pre-, Post- and Delta-Radiomic Features and Machine Learning Algorithms in Colorectal Cancer

We evaluate the feasibility of treatment response prediction using MRI-based pre-, post- and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT)

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

Objectives The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). Results The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. Conclusions The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347′259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external reverse transcription-polymerase chain reaction positive COVID-19 dataset (7′333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98–0.99) and 0.91 ± 0.038 (95% CI, 0.90–0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, −0.12 to 0.18) and −0.18 ± 3.4% (95% CI, −0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16–0.59) and 0.81 ± 6.6% (95% CI, −0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first-order feature (−6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification

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