Drowning is the third most common cause of accidental death in children. Various predictors for survival and neurological dysfunction include the age of victim, submersion time, salt versus fresh water, temperature of water, cardiopulmonary resuscitation at the scene, and time required to hospital arrival. Autonomic dysfunction, in particular heart rate variability in a critically ill patient, has been attributed to good neurological outcomes. We hereby describe a 10-year-old boy who accidentally drowned and developed a substantial heart rate variability with autonomic dysfunction. He was in the need for prolonged vasopressor support but eventually had a good neurological outcome.

Drowning is the third most common cause of accidental death in children. Various predictors for survival and neurological dysfunction include the age of victim, submersion time, salt versus fresh water, temperature of water, cardiopulmonary resuscitation at the scene, and time required to hospital arrival. Autonomic dysfunction, in particular heart rate variability in a critically ill patient, has been attributed to good neurological outcomes. We hereby describe a 10-year-old boy who accidentally drowned and developed a substantial heart rate variability with autonomic dysfunction. He was in the need for prolonged vasopressor support but eventually had a good neurological outcome.

Drowning is the third most common cause of accidental death in children. Various predictors for survival and neurological dysfunction include the age of victim, submersion time, salt versus fresh water, temperature of water, cardiopulmonary resuscitation at the scene, and time required to hospital arrival. Autonomic dysfunction, in particular heart rate variability in a critically ill patient, has been attributed to good neurological outcomes. We hereby describe a 10-year-old boy who accidentally drowned and developed a substantial heart rate variability with autonomic dysfunction. He was in the need for prolonged vasopressor support but eventually had a good neurological outcome.

Drowning is the third most common cause of accidental death in children. Various predictors for survival and neurological dysfunction include the age of victim, submersion time, salt versus fresh water, temperature of water, cardiopulmonary resuscitation at the scene, and time required to hospital arrival. Autonomic dysfunction, in particular heart rate variability in a critically ill patient, has been attributed to good neurological outcomes. We hereby describe a 10-year-old boy who accidentally drowned and developed a substantial heart rate variability with autonomic dysfunction. He was in the need for prolonged vasopressor support but eventually had a good neurological outcome.

Drowning is the third most common cause of accidental death in children. Various predictors for survival and neurological dysfunction include the age of victim, submersion time, salt versus fresh water, temperature of water, cardiopulmonary resuscitation at the scene, and time required to hospital arrival. Autonomic dysfunction, in particular heart rate variability in a critically ill patient, has been attributed to good neurological outcomes. We hereby describe a 10-year-old boy who accidentally drowned and developed a substantial heart rate variability with autonomic dysfunction. He was in the need for prolonged vasopressor support but eventually had a good neurological outcome.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Background: In recent years, medical thoracoscopy has been well established to play an important role in undiagnosed pleural effusion; however, this procedure is underutilized due to limited availability of the instruments it requires. This study analysed the outcome of single port rigid thoracoscopy in patients with undiagnosed pleural effusions. Methods: This study retrospectively analysed the outcomes of all patients with undiagnosed pleural effusion presenting to our centre between 2016 to 2020 who underwent single port rigid medical thoracoscopy as a diagnostic procedure. Results: In total, 92 patients underwent single port rigid medical thoracoscopy. The most common presenting symptom was shortness of breath. A majority of the patients had lymphocytic exudative pleural effusion. The average biopsy sample size was 18 mm, and no major complication was reported in any of the patients. Conclusion Single port rigid thoracoscopy is a safe and well-tolerated procedure that yields a biopsy of a larger size with high diagnostic yield. Moreover, the low cost of the instruments required by this procedure makes it particularly suited for use in developing countries.

Methods: We analyzed 124 operated CSM cases from March 2019 to April 2021 for preoperative clinical features, cervical sagittal radiographic parameters, and magnetic resonance imaging (MRI) signal intensities (SI). The risk factors associated with missing the MCID (poor outcome) on mJOA at the final follow-up were identified using binary logistic regression. Multivariate analysis was used to find significant risk factors, and odds ratios (OR) were computed. Results: A total of 110 men (89.2%) and 14 women (10.8%) with an average age of 53.5±13.2 years were included in the analysis. During the last follow-up, 89 cases (72.1%) achieved MCID (meaningful gains following surgery) while 35 (27.9%) could not. The final model identified the following parameters as significant risk factors for poor outcome: increased duration of symptoms (OR, 6.77; p=0.001), lower preoperative mJOA scale (OR, 0.75; p=0.029), the presence of multilevel T2-weighted (T2W) MRI SI (OR, 4.79; p=0.004), and larger change in cervical sagittal vertical axis (ΔcSVA) (OR, 1.06; p=0.013). Also, an increase in cSVA postoperatively correlated with a reduced functional recovery rate (r=−0.4, p<0.001). Conclusions Surgery for CSM leads to significant functional benefits. However, poorer outcomes are observed in cases of greater duration of symptoms, higher preoperative severity with multilevel T2W MRI SI, and a larger increase in the postoperative cSVA (sagittal imbalance).