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Research Article
Minimally Invasive Inflatable Mediastinoscope-Assisted Laparoscopic Esophagectomy for Esophageal Cancer: Case Series and Review
Issue:
Volume 13, Issue 3, June 2025
Pages:
35-39
Received:
20 March 2025
Accepted:
27 April 2025
Published:
14 May 2025
Abstract: Background: Inflatable mediastinoscope-assisted laparoscopic esophagectomy (IMLE) is an innovative minimally invasive technique for esophageal cancer, offering reduced postoperative pain, minimal intraoperative bleeding, and accelerated recovery compared to traditional open esophagectomy. It is particularly advantageous for patients with compromised pulmonary function or comorbidities precluding transthoracic approaches. Case Presentation: We present two cases of elderly male patients with esophageal squamous cell carcinoma and dysphagia, both with a history of chronic bronchitis and moderate pulmonary impairment. Following multidisciplinary team (MDT) approval, both underwent IMLE under general anesthesia using an inflatable mediastinoscope with carbon dioxide insufflation for mediastinal dissection and lymph node clearance. One patient recovered uneventfully, while the other developed a postoperative pulmonary infection, which was successfully managed. Conclusion: IMLE provides significant benefits, including reduced morbidity, shorter recovery times, and effective oncological outcomes through comprehensive lymph node dissection. However, it demands specialized equipment and expertise, and potential complications necessitate meticulous patient selection. Further research is required to optimize this technique and broaden its clinical applicability.
Abstract: Background: Inflatable mediastinoscope-assisted laparoscopic esophagectomy (IMLE) is an innovative minimally invasive technique for esophageal cancer, offering reduced postoperative pain, minimal intraoperative bleeding, and accelerated recovery compared to traditional open esophagectomy. It is particularly advantageous for patients with compromise...
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Research Article
Ear Packing Vs. Standard Drops in Otitis Externa: Superior Outcomes in Obstructed Cases
Issue:
Volume 13, Issue 3, June 2025
Pages:
40-44
Received:
27 April 2025
Accepted:
13 May 2025
Published:
27 May 2025
Abstract: Background: Otitis externa (OE) is a common condition often treated with topical antibiotics and corticosteroids. However, the effectiveness of ear drops can be limited in cases involving canal obstruction, poor patient compliance, or anatomical variations that hinder proper medication delivery. There is a growing interest in alternative delivery methods that ensure more consistent drug application and faster symptom resolution. Objective: To compare the efficacy of hydrocortisone-oxytetracycline ear packing using Hydrocyclin® ointment with Paroticin® ear drops in treating uncomplicated and obstructed otitis externa (OE). Methods: A prospective cohort study of 200 patients was conducted. Patients were allocated into packing (n=100) and drops (n=100) groups. Outcomes included clinical resolution, pain reduction, and analgesic use. Results: For uncomplicated OE, packing achieved 100% resolution by Day 6, versus 70% with drops by Day 7 (p<0.001). In obstructed canals, packing resolved 100% by Day 6 versus 35% with drops (p<0.001). Pain reduction was faster with packing, with 80% reduction by 48 hours compared to 45% in the drops group (p<0.001). Analgesic use decreased more rapidly in the packing group. Conclusion: Hydrocortisone-oxytetracycline ear packing demonstrates superior clinical outcomes compared to standard ear drop therapy, particularly in cases of canal obstruction. Packing ensures more consistent drug delivery, better symptom relief, and faster recovery. This method also improves clinician control over treatment administration and may benefit patients with impaired compliance or anatomical challenges.
Abstract: Background: Otitis externa (OE) is a common condition often treated with topical antibiotics and corticosteroids. However, the effectiveness of ear drops can be limited in cases involving canal obstruction, poor patient compliance, or anatomical variations that hinder proper medication delivery. There is a growing interest in alternative delivery m...
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Research Article
Depression Predictive Model Using In-Context Learning Based on HRV with PPG Derived Validity Label
Issue:
Volume 13, Issue 3, June 2025
Pages:
45-53
Received:
25 March 2025
Accepted:
14 April 2025
Published:
11 June 2025
DOI:
10.11648/j.ajcem.20251303.13
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Views:
Abstract: Background: Traditional diagnostic approaches for major depression disorder (MDD) or clinical depression rely on subjective assessment of clinical symptoms while heart rate variability (HRV) metrics provide an objective alternative to support clinical assessments and facilitate early depression detection. However, the imperceptibility of non-stationarity and unpredictability in noticing a factor for its HRV outcome highlight the challenges in modelling of predictive AI. Methods: In this study, totally 139 participants were recruited including 40 patients and 99 healthy controls. Only 28 of the 40 depression patients and 34 of the 99 healthy controls were enrolled for HRV data collection according to inclusion criteria. Our experiment provided evidence for evaluation of the validation method using a photoplethysmography (PPG) derived parameter representing beat-to-beat stress-induced vascular response in terms of labelling performance and applicability. Results: The results demonstrated the link between depression and the autonomic nervous system (ANS) measured using HRV both in statistical analysis and AI-driven classification, as seen in the GPT-4-based LLM outperformed baseline models across multiple data sets. The validity labeling contributed significantly to model performance and robustness, especially in small-sample scenarios. Although small sample size was used in HRV-based depression prediction training via a large language model (LLM) with in-context learning (ICL), the performance was definitely improved with validity labeling activated compared to labeling disabled. Conclusions: Through comparison of observational accuracy in predictive models, the reliability of HRV recordings is crucial for improving AI-driven depression prediction and aligning AI analysis with the expectations on physiological and psychological effects. Among factors that could cause HRV value to change in unexpected ways, stationarity is a prerequisite for short-term HRV (ST-HRV), thus validation strategy, a labeling method capable of identifying and rejecting recordings of false signals, is necessarily needed.
Abstract: Background: Traditional diagnostic approaches for major depression disorder (MDD) or clinical depression rely on subjective assessment of clinical symptoms while heart rate variability (HRV) metrics provide an objective alternative to support clinical assessments and facilitate early depression detection. However, the imperceptibility of non-statio...
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,
Feng Liu
