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Background: In the current treatment landscape for non-small cell lung cancers, epidermal growth factor receptor-tyrosine kinase inhibitors have emerged as a well-established treatment option for patients with advanced or metastatic disease. This is particularly true for those with commonly occurring epidermal growth factor receptor mutations. However, the therapeutic efficacy of these agents for so-called rare epidermal growth factor receptor mutations, and in particular those characterized by a high degree of complexity, such as double mutations, remains a subject of clinical uncertainty., Case Presentation: In this context, we present the case of a 64-year-old man of Moroccan descent, a lifelong non-smoker, diagnosed with metastatic non-small cell lung cancer characterized by a complex epidermal growth factor receptor mutation encompassing L858R and S768I. The patient subsequently underwent afatinib-based treatment, showing notable clinical results. These included a remarkable overall survival of 51 months, with a median progression-free survival of more than 39 months., Conclusions: This case report is a compelling testimony to the evolving therapeutic landscape of non-small cell lung cancers, providing valuable insight into the potential therapeutic efficacy of epidermal growth factor receptor-tyrosine kinase inhibitors in the realm of rare and complex epidermal growth factor receptor mutations., (© 2024. The Author(s).)

Objective: Mental imagery (MI) has long been used in learning in both fields of sports and arts. However, it is restrictively applied in surgical training according to the medical literature. Few studies have evaluated its' feasibility and usefulness. The aim of this study is to assess the impact of mental imagery on surgical skills learning among novice's surgeons., Material and Methods: In this pilot prospective randomized comparative study; we recruited 17 residents and interns of surgery education curriculum. They were all included in their first semester of the curricula. Two groups were randomly designed. Group (a) including "Mental Imagery" volunteers (n = 9) which benefited from a mental imagery rehearsal exercise prior to physical practice, while the control group (b) (n = 8) didn't underwent any MI process prior to surgery practice. Each participant of both groups was invited to perform an intestinal hand-sewn anastomosis on bovine intestine. Each procedure was evaluated and analyzed according to 14 qualitative criteria while each criterion was scored 0, 1 or 2 respectively assigned to the gesture was not acquired, gesture was performed with effort, or mastered gesture. The final score is 28 for those who master all 14 gestures. A non-parametric statistical comparison between the both studied groups was performed., Results: Both groups of surgery students demonstrated equivalent age, sex ratio, laterality, and surgical experience. The mean overall score is significantly higher in the MI group (a) (17.78; SD = 2.42) compared to the control group (b) (10.63, SD = 2.85). However, advanced analysis of individual assessment items showed significant statistical difference between both groups only in 6 out of 14 assessed items., Conclusion: Indeed, mental imagery will not be able to substitute the traditional learning of surgery for novice surgeons; it is an important approach for improving the technical skills acquisition and shortening the physical learning., (© 2021. The Author(s).)

Summary: Objective: To evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma. Methods: A total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes. Results: Statistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05). Conclusions: The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups. [ABSTRACT FROM AUTHOR]

Background: Morocco faces a substantial public health challenge due to diabetes mellitus, affecting 12.4% of adults in 2023. The Moroccan population makes extensive use of phytotherapy and traditional medicine to address the difficulties this chronic condition poses. The aim of this study is to document the use of medicinal plants in traditional medicine for managing type 2 diabetes in the provinces of the Casablanca-Settat region. Methods: The study employed a semi-structured questionnaire for data collection. A study was conducted between August 1st and September 30th, 2023, and 244 individuals diagnosed with diabetes were invited to take part in the research, all of whom used at least one medicinal plant to manage type 2 diabetes, by visiting primary healthcare facilities in Morocco. The analysis included the use of Relative Frequency of Citation (RFC) to scrutinize the data. Results: A total of 47 plant species belonging to 25 families were documented. Notably, the Apiaceae, Lamiaceae, and Fabaceae families were frequently mentioned in the context of treating type 2 diabetes in Morocco. Prominent among the cited plant species were Sesamum indicum L., Lepidium sativum L., followed by Foeniculum vulgare Mill., and Rosmarinus officinalis L. Seeds emerged as the plant part most commonly mentioned, with infusion being the prevailing preparation method and oral consumption being the most frequently depicted method of administration. Conclusion: This research underscores the practicality of incorporating traditional medicine into the healthcare framework of the Casablanca-Settat region. The findings not only offer valuable documentation but also have a vital function in safeguarding knowledge regarding the utilization of medicinal plants in this locality. Moreover, they provide opportunities to delve deeper into the phytochemical and pharmacological potential of these plants. [ABSTRACT FROM AUTHOR]

Background: Pancreatic pseudocyst are fluid filled sacs that develop as a result of dissection of pancreatic enzyme tissue. While most commonly found near the pancreas, they can also rarely occur in other areas such as the perirenal region. Case presentation: This study reports a new case of an infected perirenal pancreatic pseudocyst mimicking a left kidney abscess in a 46-year-old North African patient with history of recurrent acute pancreatitis, who presented with left lumbar region pain and fever. Computed tomography revealed a left perirenal collection that turned out to be an infected pancreatic pseudocyst, The diagnostic was first suspected based on the medical history of the patient and confirmed by biochemical examination detecting a high level of pancreatic enzymes in the computed tomography-guided percutaneous drainage fluid. The patient evolved well after early resuscitation, rapid and effective antibiotic therapy, and computed tomography-guided percutaneous drainage of renal collection. Conclusion: Pancreatic pseudocyst is an uncommon disorder, which may present at a complicated stage and that must be considered in patients with a history of pancreatitis. [ABSTRACT FROM AUTHOR]

Background: Pneumomediastinum is a rare complication of COVID-19 pneumonia, which may or may not be associated with invasive ventilatory support. Therefore, the report and findings associated with its evolution can be of great contribution in the management of this unknown disease. Case presentation: Here, we present a series of four patients with severe pneumomediastinum requiring intensive care unit. These patients developed pneumomediastinum before or during orotracheal intubation (OTI) or without OTI. The four patients were three men and one woman with a mean age of 60.5 years (48–74 years). No patients had a known history of lung disease or traumatic events, except for one patient who had a history of smoking, but who was without parenchymal disease. All intubations were performed without complications. No cases of pneumomediastinum occurred after tracheostomy, and none of the patients had tomographic or bronchoscopic evidence of tracheal injury. Although the pneumomediastinum observed in our cases was apparently not related to a violation of the aerodigestive track, this complication was associated with a worse prognosis. Conclusion: Pneumomediastinum is a rare complication of COVID-19 pneumonia, and the most likely etiopathogenesis is severe pulmonary involvement, which may or may not be associated with invasive ventilatory support. Future studies with a greater number of cases should elucidate the relationship of pneumomediastinum to a probable prognostic factor. [ABSTRACT FROM AUTHOR]

Background: As the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the contrast-unenhanced time-of-flight magnetic resonance angiography is one of the most commonly used methods for screening aneurysms. The computer-assisted detection system for cerebral aneurysms can help clinicians improve the accuracy of aneurysm diagnosis. As fully convolutional network could classify the image pixel-wise, its three-dimensional implementation is highly suitable for the classification of the vascular structure. However, because the volume of blood vessels in the image is relatively small, 3D convolutional neural network does not work well for blood vessels.Results: The presented study developed a computer-assisted detection system for cerebral aneurysms in the contrast-unenhanced time-of-flight magnetic resonance angiography image. The system first extracts the volume of interest with a fully automatic vessel segmentation algorithm, then uses 3D-UNet-based fully convolutional network to detect the aneurysm areas. A total of 131 magnetic resonance angiography image data are used in this study, among which 76 are training sets, 20 are internal test sets and 35 are external test sets. The presented system obtained 94.4% sensitivity in the fivefold cross-validation of the internal test sets and obtained 82.9% sensitivity with 0.86 false positive/case in the detection of the external test sets.Conclusions: The proposed computer-assisted detection system can automatically detect the suspected aneurysm areas in contrast-unenhanced time-of-flight magnetic resonance angiography images. It can be used for aneurysm screening in the daily physical examination. [ABSTRACT FROM AUTHOR]