Combination of Anterior and Posterior Vaginal Mesh Implants and Delorme Procedure in a 94-year-old female with Pelvic Organ Prolapse.

Pelvic organ prolapse refers to the descent of pelvic floor organs resulting from the weakening of pelvic muscles, fascia and connective tissue. The overall prevalence of pelvic organ prolapse is approximately 41%, including bladder prolapse (25%-34%), uterine prolapse (4%-14%) and rectal prolapse (13%-19%). Various methods are currently employed to repair damaged structures and improve patient symptoms, consequently enhancing their quality of life. This report focuses on a 94-year-old female diagnosed with pelvic organ prolapse, specifically Grade 3 bladder prolapse, Grade 3 uterine prolapse and complete rectal prolapse. A comprehensive surgical treatment was carried out to repair the pelvic organs on all three levels (rectum, uterus and bladder) by combining the Delorme procedure with synthetic graft implants. The surgical outcomes were good, illustrating immediate improvement in symptoms without early complications. A multispeciality approach helps functionally repair pelvic organ prolapse while preserving structural integrity.

Performance and limitations of a supervised deep learning approach for the histopathological Oxford Classification of glomeruli with IgA nephropathy.

BACKGROUND AND OBJECTIVE: The Oxford Classification for IgA nephropathy is the most successful example of an evidence-based nephropathology classification system. The aim of our study was to replicate the glomerular components of Oxford scoring with an end-to-end deep learning pipeline that involves automatic glomerular segmentation followed by classification for mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental sclerosis (S) and active crescents (C). METHODS: A total number of 1056 periodic acid-Schiff (PAS) whole slide images (WSIs), coming from 386 kidney biopsies, were annotated. Several detection models for glomeruli, based on the Mask R-CNN architecture, were trained on 587 WSIs, validated on 161 WSIs, and tested on 127 WSIs. For the development of segmentation models, 20,529 glomeruli were annotated, of which 16,571 as training and 3958 as validation set. The test set of the segmentation module comprised of 2948 glomeruli. For the Oxford classification, 6206 expert-annotated glomeruli from 308 PAS WSIs were labelled for M, E, S, C and split into a training set of 4298 glomeruli from 207 WSIs, and a test set of 1908 glomeruli. We chose the best-performing models to construct an end-to-end pipeline, which we named MESCnn (MESC classification by neural network), for the glomerular Oxford classification of WSIs. RESULTS: Instance segmentation yielded excellent results with an AP50 ranging between 78.2-80.1 % (79.4 ± 0.7 %) on the validation and 75.1-77.7 % (76.5 ± 0.9 %) on the test set. The aggregated Jaccard Index was between 73.4-75.9 % (75.0 ± 0.8 %) on the validation and 69.1-73.4 % (72.2 ± 1.4 %) on the test set. At granular glomerular level, Oxford Classification was best replicated for M with EfficientNetV2-L with a mean ROC-AUC of 90.2 % and a mean precision/recall area under the curve (PR-AUC) of 81.8 %, best for E with MobileNetV2 (ROC-AUC 94.7 %) and ResNet50 (PR-AUC 75.8 %), best for S with EfficientNetV2-M (mean ROC-AUC 92.7 %, mean PR-AUC 87.7 %), best for C with EfficientNetV2-L (ROC-AUC 92.3 %) and EfficientNetV2-S (PR-AUC 54.7 %). At biopsy-level, correlation between expert and deep learning labels fulfilled the demands of the Oxford Classification. CONCLUSION: We designed an end-to-end pipeline for glomerular Oxford Classification on both a granular glomerular and an entire biopsy level. Both the glomerular segmentation and the classification modules are freely available for further development to the renal medicine community.

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction.

Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.

Prehospital antibiotics in the ambulance for sepsis: a multicentre, open label, randomised trial.

BACKGROUND: Emergency medical services (EMS) personnel have already made substantial contributions to improving care for patients with time-dependent illnesses, such as trauma and myocardial infarction. Patients with sepsis could also benefit from timely prehospital care. METHODS: After training EMS personnel in recognising sepsis, we did a randomised controlled open-label trial in ten large regional ambulance services serving 34 secondary and tertiary care hospitals in the Netherlands. We compared the effects of early administration of antibiotics in the ambulance with usual care. Eligible patients were randomly assigned (1:1) using block-randomisation with blocks of size 4 to the intervention (open-label intravenous ceftriaxone 2000 mg in addition to usual care) or usual care (fluid resuscitation and supplementary oxygen). Randomisation was stratified per region. The primary outcome was all-cause mortality at 28 days and analysis was by intention to treat. To assess the effect of training, we determined the average time to antibiotics (TTA) in the emergency department and recognition of sepsis by EMS personnel before and after training. The trial is registered at ClinicalTrials.gov, number NCT01988428. FINDINGS: 2698 patients were enrolled between June 30, 2014, and June 26, 2016. 2672 patients were included in the intention-to-treat analysis: 1535 in the intervention group and 1137 in the usual care group. The intervention group received antibiotics a median of 26 min (IQR 19-34) before arriving at the emergency department. In the usual care group, median TTA after arriving at the emergency department was 70 min (IQR 36-128), compared with 93 min (IQR 39-140) before EMS personnel training (p=0·142). At day 28, 120 (8%) patients had died in the intervention group and 93 (8%) had died in the usual care group (relative risk 0·95, 95% CI 0·74-1·24). 102 (7%) patients in the intervention group and 119 (10%) in the usual care group were re-admitted to hospital within 28 days (p=0·0004). Seven mild allergic reactions occurred, none of which could be attributed to ceftriaxone. INTERPRETATION: In patients with varying severity of sepsis, EMS personnel training improved early recognition and care in the whole acute care chain. However, giving antibiotics in the ambulance did not lead to improved survival, regardless of illness severity. FUNDING: The NutsOhra Foundation, Netherlands Society of Internal Medicine (NIV).

Modulation of microenvironmental pH for dual release and reduced in vivo gastrointestinal bleeding of aceclofenac using hydroxypropyl methylcellulose-based bilayered matrix tablet.

This study was designed to develop a once-daily controlled-release matrix tablet of aceclofenac 200mg (AFC-CR) with dual release characteristics and to investigate the role of an alkalizer in enhancing drug solubility and reducing the occurrence of gastroduodenal mucosal lesions. Two formulation approaches were employed, namely a monolithic matrix tablet and a bilayered tablet. In vitro dissolution studies of AFC-CR tablets were carried out in simulated intestinal fluid (pH6.8 buffer). The in vivo pharmacokinetic studies and drug safety of the immediate-release reference tablet Airtal® 100mg (Daewoong Co., Korea) and the optimized AFC-CR tablet were compared in beagle dogs under fasted condition. The optimally selected AFC-CR formulation displayed the desired dual release characteristics in simulated intestinal fluid with satisfactory micromeritic properties. The swelling action of the optimal matrix tablet, which was visualized by near-infrared (NIR) chemical imaging, occurred rapidly following hydration. Incorporation of sodium carbonate (Na2CO3) was found to enhance the release rate of the AFC-CR bilayered tablets at early stages and increase the microenvironmental pH (pHM). A pharmacokinetic study in beagle dogs indicated a higher drug plasma concentration and a sustained-release pattern for the AFC-CR tablet compared to the Airtal® tablet. AFC-CR was also superior to Airtal® in terms of in vivo drug safety, since no beagle dog receiving AFC-CR experienced gastrointestinal bleeding. The significant enhancement of drug safety was attributed to the size reduction and the increase of pHM of drug particles by means of incorporation of the alkalizer. These findings provide a scientific rationale for developing a novel controlled-release matrix tablet with enhanced patient compliance and better pain control.