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.

INTRODUCTION: The National Institute of Health and Care Excellence (NICE) has defined the terms, 'acute coronavirus disease 2019' (COVID-19), 'ongoing symptomatic COVID-19' and 'post-COVID-19 syndrome', with the latter two described as having persistent symptoms after the onset of COVID-19 symptoms for 4-12 weeks and >12 weeks, respectively. Persistent symptoms can either be due to the after-effects of COVID-19 or new-onset diseases after acute COVID-19. All symptoms observed beyond 4 weeks after the onset of COVID-19 need not be present at the time of onset. Previous studies on persistent post-COVID-19 symptoms have not mentioned new-onset diseases after acute COVID-19, and only a select few studies have discussed such new-onset symptoms. METHODS: Ninety-five patients who attended the post-COVID-19 clinic completed the requisite follow-up till 16 weeks after COVID-19 symptom onset. Data was recorded on a predesigned proforma. Necessary investigations were conducted to rule out any other cause of persistent symptoms. RESULTS: Fatigue (62.1%), breathlessness (50.5%) and cough (27.4%) were the most common symptoms present beyond 4 weeks after the onset of COVID-19 symptoms. Forty-nine (51.57%) patients developed post-COVID-19 syndrome - their severity of symptoms (odds ratio [OR] 17.77) and longer duration of hospital stay (OR 1.095) during acute disease were significantly associated with the development of post-COVID-19 syndrome. During follow-up, 25 patients developed new-onset symptoms, such as diabetes mellitus, hypertension and idiopathic tachycardia. CONCLUSION Patients can have persistent symptoms, new-onset symptoms and new-onset diseases after recovery from acute COVID-19.
Humans ; COVID-19/diagnosis* ; Female ; Male ; Prospective Studies ; Middle Aged ; Adult ; Fatigue/etiology* ; Post-Acute COVID-19 Syndrome ; SARS-CoV-2 ; Aged ; Cough/etiology* ; Dyspnea/etiology*

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.