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To evaluate and compare the analgesic effect of ultrasound-guided erector spinae plane (ESP) block with ultrasound-guided Quadratus Lumborum Block in pediatric lower abdominal surgeries. Randomized, prospective, double-blinded trial. Operating room and surgical ward. Sixty patients, aged 1 to 7 years with ASA scores of I-II scheduled for elective lower abdominal surgery were included in the study. Patients were randomized into two groups as ESPB group and QLB group. Ultrasound guided ESP block at L1 vertebral level was performed preoperatively using 0.5 ml/kg 0.25% bupivacaine (max 20 ml) to the patients in ESPB group. And ultrasound guided QLB block with transmuscular approach was performed preoperatively using 0.5 ml/kg 0.25% bupivacaine (max 20 ml) to the patients in QLB group. Face, Legs, Activity, Cry and Consolability (FLACC) scores for pain were recorded at 0, 1, 3 and 6 h postoperatively. Analgesic requirements and time to first analgesic requirement were also recorded. Fifty-seven patients were included in the final analyses. No significant difference was determined between the groups' FLACC scores at 0, 1, 3 or 6 h postoperatively (p > 0.05). No significant difference was also determined in times to first analgesia between the groups (p > 0.05). This study shows that the ESPB provides similar postoperative analgesia to the QLB in pediatric patients undergoing lower abdominal surgery. Clinicians could decide according to their clinical experiences. https://clinicaltrials.gov/ct2/show/NCT03463382. •Erector Spinae Plane Block is a novel technique for postoperative analgesia in many indications.•Quadratus Lumborum Block is a well known analgesic technique for different pediatric surgeries.•Erector Spinae Plane Block provides similar analgesia to Quadratus Lumborum Block for pediatric lower abdominal surgeries.•Opioid sparing effect could be achieved with both Quadratus Lumborum and Erector Spinae Plane blocks in pediatric surgeries.

Background/purpose The inflammatory response that follows the caustic burns results in fibrosis on the esophageal wall leading to esophageal stricture, dysphagia, and malnutrition. The controversy over the use of corticosteroids warrants alternative therapeutic interventions. We investigated the effect of extracts from St. John’s wort (SJW) with known wound-healing activity on stricture formation in rat esophageal injury models. Methods Five experimental groups were involved: sham group with no injury, control group with injury without treatment, and three different treatment groups (methylprednisolone, SJW extract, and combination of the two). Histopathological examination of esophageal damage and collagen accumulation, stenosis index, and tissue hydroxyproline levels were used to assess stricture and the effect of treatments. Results There was a significant weight loss in all groups except for those without injury and those treated with SJW extract, the latter gained weight albeit not significant. Stenosis index was increased in all groups compared to sham but not significantly in those treated with SJW extract. Histopathological and biochemical analyses produced mixed results. Conclusions Some of the experimental indicators such as weight gain and stenosis index suggested the treatment of esophageal injury models using extracts of St. John’s wort effective while other histopathological indicators show no significant benefit.

Introduction. In recent years, there has been a significant rise in the number of pediatric cases involving multiple magnet ingestion, resulting in increased incidence and morbidity of injuries. When a metal object and magnet are ingested, either single or multiple, they can cause serious complications such as intestinal obstruction, ischemia, necrosis, fistula, perforation, and even death. This study aims to detail the complications and treatment approaches associated with magnet ingestion in children. Materials and Methods. In our study, we conducted a retrospective analysis of all cases involving the ingestion of a magnet along with a second metal object at two training and research hospitals in our province, which admit pediatric patients, between the years of 2013 and 2023. Results. A total of 42 patients had a history of magnet ingestion, with the number of ingested magnets ranging from 1 to 41. The median magnet size was 11 mm (range: 5.5-17.5 mm) and the median time to presentation was 24 hours (range: 3-48 hours). Thirteen patients (30.9%) required either endoscopic or surgical intervention to extract the magnets or address complications. Endoscopy was performed on eight patients, while surgical intervention was required for five patients. Among those who underwent surgery, four experienced complications, including intestinal perforation, ileoileal fistula, and internal herniation. Notably, no fatalities occurred following intervention. There was no statistically significant difference in age or magnet size between the interventional and non-interventional groups. However, the length of hospital stay was significantly longer in the interventional group compared to the non-interventional group (P

Objective This study presents DraiNet, a deep learning model developed to detect pneumothorax and pleural effusion in pediatric patients and aid in assessing the necessity for tube thoracostomy. The primary goal is to utilize DraiNet as a decision support tool to enhance clinical decision-making in the management of these conditions. Methods DraiNet was trained on a diverse dataset of pediatric CT scans, carefully annotated by experienced surgeons. The model incorporated advanced object detection techniques and underwent evaluation using standard metrics, such as mean Average Precision (mAP), to assess its performance. Results DraiNet achieved an impressive mAP score of 0.964, demonstrating high accuracy in detecting and precisely localizing abnormalities associated with pneumothorax and pleural effusion. The model’s precision and recall further confirmed its ability to effectively predict positive cases. Conclusion The integration of DraiNet as an AI-driven decision support system marks a significant advancement in pediatric healthcare. By combining deep learning algorithms with clinical expertise, DraiNet provides a valuable tool for non-surgical teams and emergency room doctors, aiding them in making informed decisions about surgical interventions. With its remarkable mAP score of 0.964, DraiNet has the potential to enhance patient outcomes and optimize the management of critical conditions, including pneumothorax and pleural effusion.

Simple epididymal cysts (EC) are rare in childhood and are mostly diagnosed at puberty. Although there is no consensus on the treatment, a conservative approach is generally preferred. To evaluate patients diagnosed with EC at our clinic in terms of presenting symptoms, diagnosis, and treatment methods. Data of patients treated for epididymal cysts at our institution between March 2012 and March 2023 were retrospectively analyzed in terms of age, symptomatology, diagnostic method, treatment method, and outcomes. In all cases, the diagnosis of EC was based on physical examination with scrotal ultrasonography (US) confirmation. A total of 1829 patients underwent scrotal Doppler US, and EC was detected in 72 patients (10.7%). The median follow-up period of the 43 patients was 21.7 (6-80 months). Of these, 9 were bilateral (12.5%). The mean age of the patients at presentation was 14.8 years. Forty-one patients had scrotal pain, 12 had scrotal swelling, and 19 incidentally had EC. The cysts were between 1.2- 37 mm. Only 3 (4.1%) patients required surgical excision due to persistent pain. EC is a benign lesion, and treatment approaches are usually conservative. Surgical excision is recommended for patients with persistent scrotal pain or an acute scrotum.

Preoperative differentiation of Wilms tumor and neuroblastoma on pediatric abdominal computed tomography (CT) images may be challenging because of overlapping imaging features. We aimed to develop an artificial intelligence-assisted lesion-localization model for exploratory diagnostic support in this differential setting. In this single-center, retrospective, image-level study, a YOLO26s detector was trained on preoperative contrast-enhanced CT PNG images with histopathology-anchored labels. The dataset comprised 3553 images, including 2103 lesion-positive images and 1450 background-negative images, partitioned into training, validation, and test subsets. On the held-out test set, the model achieved a precision of 0.954, a recall of 0.951, an mAP@0.5 of 0.977, and an mAP@0.5:0.95 of 0.732. Class-specific mAP@0.5:0.95 values were 0.734 for neuroblastoma and 0.730 for Wilms tumor. At the image level, tumor-present versus background-negative discrimination yielded 99.5% sensitivity, 89.0% specificity, a 93.0% positive predictive value, a 99.2% negative predictive value, and 95.3% accuracy. YOLO26s showed strong within-dataset performance for lesion localization and differential support between Wilms tumor and neuroblastoma.

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system’s potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.

Congenital absence of internal carotid artery is a relatively rare disorder. It may be an isolated entity or combined with other organ or system anomalies. Investigations of a 19-year-old male patient with hypogonadotropic hypogonadism and hypothyroidism suffering from intracerebral and subarachnoid hemorrhages showed absence of the left internal carotid artery. The association of endocrinologic disorders and congenital absence of internal carotid artery is discussed with the pertinent literature. In conclusion, association of the absence of internal carotid artery and hypogonadism is extremely rare. Patients with absence of internal carotid artery may also have intracerebral and subarachnoid hemorrhages and should be followed up closely.