Comparison of hysteroscopic and laparoscopic myomectomy in large type 2 submucous leiomyomas

Background: Uterine leiomyomas are the most common benign tumors, affecting 30% of women of reproductive age. Submucous myomas are seen in 5.5-10% of all myomas. This study aimed to compare clinical, peri, and post-op outcomes of hysteroscopic and laparoscopic myomectomy in large type 2 submucous myomas. Methods: A prospective study was performed on 50 patients with large submucous type 2 leiomyomas measuring 3-5cm from October 2020 to August 2022. Patients were randomized into two groups of 25 each. Group A underwent hysteroscopic myomectomy and group B underwent laparoscopic myomectomy. Results: There was no significant difference in the demographic data of both groups except parity. Perioperative outcomes including bleeding, pain, and hospital stay were significantly higher in the laparoscopy group. None of our patients had air embolism. One patient had blindness in the postoperative period. 2 patients had uterine perforation in the hysteroscopy group. Postoperative pain was higher in the laparoscopy group. Recurrence at 3 months was seen in 2 patients of group A. Asherman syndrome was seen in group A. Single-stage success rate was seen higher in the laparoscopy group. Conclusions: Laparoscopy and hysteroscopy both are feasible techniques of myomectomy for submucous leiomyomas but for removal of large submucous leiomyomas laparoscopy myomectomy is considered better. For successful removal of large myomas in single-stage hysteroscopy, use of hysteroscopic morcellation should be considered

Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients

Machine learning (ML) models are increasingly pivotal in automating clinical decisions. Yet, a glaring oversight in prior research has been the lack of proper processing of Electronic Medical Record (EMR) data in the clinical context for errors and outliers. Addressing this oversight, we introduce an innovative projections-based method that seamlessly integrates clinical expertise as domain constraints, generating important meta-data that can be used in ML workflows. In particular, by using high-dimensional mixed-integer programs that capture physiological and biological constraints on patient vitals and lab values, we can harness the power of mathematical "projections" for the EMR data to correct patient data. Consequently, we measure the distance of corrected data from the constraints defining a healthy range of patient data, resulting in a unique predictive metric we term as "trust-scores". These scores provide insight into the patient's health status and significantly boost the performance of ML classifiers in real-life clinical settings. We validate the impact of our framework in the context of early detection of sepsis using ML. We show an AUROC of 0.865 and a precision of 0.922, that surpasses conventional ML models without such projections

A comparative study for assessment of post-operative sequelae following mandibular transalveolar molar extractions using ozone and dexamethasone

Objectives: Trans-alveolar extractions involve surgical removal of tooth that has its own postoperative sequelae, most commonly manifested as pain and swelling. This study aims to compare the efficacy of topical ozone and dexamethasone in management of post-operative sequelae after mandibular trans-alveolar molar extractions. Materials and Methods: Sixty patients requiring surgical removal of mandibular molars under local anesthesia were randomly allocated into two groups, each group consisting of 30 patients. Group 1 received Topical Ozonated Oil in the extraction socket post-operatively, while Group 2 was administered 8 mg dexamethasone injection post-operatively. The patients were checked for postoperative wound healing, pain and swelling on 1st, 3rd and 7th day. Results: The results showed comparatively similar results for the pain severity and swelling score at the 1st and 7th postoperative day in both the groups. A greater reduction of pain was noticed in Group 1 on 3rd postoperative day. Wound Healing was noted to be better in Group 1 at 3rd and 7th post-operative day. Conclusion: In conclusion, topical ozone therapy can be used as an effective alternative treatment modality, when compared to dexamethasone for better management of post-operative sequelae following mandibular trans-alveolar molar extractions

Prospective, multicentre study of screening, investigation and management of hyponatraemia after subarachnoid haemorrhage in the UK and Ireland

Background: Hyponatraemia often occurs after subarachnoid haemorrhage (SAH). However, its clinical significance and optimal management are uncertain. We audited the screening, investigation and management of hyponatraemia after SAH. Methods: We prospectively identified consecutive patients with spontaneous SAH admitted to neurosurgical units in the United Kingdom or Ireland. We reviewed medical records daily from admission to discharge, 21 days or death and extracted all measurements of serum sodium to identify hyponatraemia (<135 mmol/L). Main outcomes were death/dependency at discharge or 21 days and admission duration >10 days. Associations of hyponatraemia with outcome were assessed using logistic regression with adjustment for predictors of outcome after SAH and admission duration. We assessed hyponatraemia-free survival using multivariable Cox regression. Results: 175/407 (43%) patients admitted to 24 neurosurgical units developed hyponatraemia. 5976 serum sodium measurements were made. Serum osmolality, urine osmolality and urine sodium were measured in 30/166 (18%) hyponatraemic patients with complete data. The most frequently target daily fluid intake was >3 L and this did not differ during hyponatraemic or non-hyponatraemic episodes. 26% (n/N=42/164) patients with hyponatraemia received sodium supplementation. 133 (35%) patients were dead or dependent within the study period and 240 (68%) patients had hospital admission for over 10 days. In the multivariable analyses, hyponatraemia was associated with less dependency (adjusted OR (aOR)=0.35 (95% CI 0.17 to 0.69)) but longer admissions (aOR=3.2 (1.8 to 5.7)). World Federation of Neurosurgical Societies grade I–III, modified Fisher 2–4 and posterior circulation aneurysms were associated with greater hazards of hyponatraemia. Conclusions: In this comprehensive multicentre prospective-adjusted analysis of patients with SAH, hyponatraemia was investigated inconsistently and, for most patients, was not associated with changes in management or clinical outcome. This work establishes a basis for the development of evidence-based SAH-specific guidance for targeted screening, investigation and management of high-risk patients to minimise the impact of hyponatraemia on admission duration and to improve consistency of patient care

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential non-invasive diagnostic procedure. This work explores the use of solid phase micro-extraction (SPME) and ambient plasma ionization mass spectrometry (MS) to rapidly acquire VOC signatures of bacteria and fungi. The MS spectrum of each pathogen goes through a preprocessing and feature extraction pipeline. Various supervised and unsupervised machine learning (ML) classification algorithms are trained and evaluated on the extracted feature set. These are able to classify the type of pathogen as bacteria or fungi with high accuracy, while marked progress is also made in identifying specific strains of bacteria. This study presents a new approach for the identification of pathogens from VOC signatures collected using SPME and ambient ionization MS by training classifiers on just a few samples of data. This ambient plasma ionization and ML approach is robust, rapid, precise, and can potentially be used as a non-invasive clinical diagnostic tool for point-of-care applications

A comparative study for assessment of post-operative sequelae following mandibular transalveolar molar extractions using ozone and dexamethasone

Objectives: Trans-alveolar extractions involve surgical removal of tooth that has its own postoperative sequelae, most commonly manifested as pain and swelling. This study aims to compare the efficacy of topical ozone and dexamethasone in management of post-operative sequelae after mandibular trans-alveolar molar extractions. Materials and Methods: Sixty patients requiring surgical removal of mandibular molars under local anesthesia were randomly allocated into two groups, each group consisting of 30 patients. Group 1 received Topical Ozonated Oil in the extraction socket post-operatively, while Group 2 was administered 8 mg dexamethasone injection post-operatively. The patients were checked for postoperative wound healing, pain and swelling on 1st, 3rd and 7th day. Results: The results showed comparatively similar results for the pain severity and swelling score at the 1st and 7th postoperative day in both the groups. A greater reduction of pain was noticed in Group 1 on 3rd postoperative day. Wound Healing was noted to be better in Group 1 at 3rd and 7th post-operative day. Conclusion: In conclusion, topical ozone therapy can be used as an effective alternative treatment modality, when compared to dexamethasone for better management of post-operative sequelae following mandibular trans-alveolar molar extractions

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or ‘equivocal’ images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the ‘equivocal’ class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems