INTRODUCTION: Degenerative mitral stenosis (DMS) is frequently cited as increasing in prevalence in the developed world, although comparatively little is known about DMS in comparison to rheumatic mitral stenosis (RMS). METHOD: A retrospective observational study was conducted on 745 cases of native-valve mitral stenosis (MS) with median follow-up time of 7.25 years. Clinical and echocardiographic parameters were compared. Univariate and multivariate Cox regression analyses were performed for a composite of all-cause mortality and heart failure hospitalisation. RESULTS: Patients with DMS compared to RMS were older (age, mean ± standard deviation: 69.6 ± 12.3 versus [vs] 51.6 ± 14.3 years, respectively; P<0.001) and a greater proportion had medical comorbidities such as diabetes mellitus (78 [41.9%] vs 112 [20.0%], P<0.001). The proportion of cases of degenerative aetiology increased from 1.1% in 1991-1995 to 41.0% in 2016-2017. In multivariate analysis for the composite outcome, age (hazard ratio [HR] 95% confidence interval [CI] of 1.032 [1.020-1.044]; P<0.001), diabetes mellitus (HR 1.443, 95% CI 1.068-1.948; P=0.017), chronic kidney disease (HR 2.043, 95% CI 1.470-2.841; P<0.001) and pulmonary artery systolic pressure (HR 1.019, 95% CI 1.010- 1.027; P<0.001) demonstrated significant indepen-dent associations. The aetiology of MS was not independently associated with the composite outcome. CONCLUSION DMS is becoming an increasingly common cause of native-valve MS. Despite numerous clinical differences between RMS and DMS, the aetiology of MS did not independently influence a composite of mortality or heart failure hospitalisation.
Humans ; Mitral Valve Stenosis/etiology* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Rheumatic Heart Disease/mortality* ; Echocardiography ; Hospitalization/statistics & numerical data* ; Heart Failure/epidemiology* ; Singapore/epidemiology* ; Proportional Hazards Models ; Diabetes Mellitus/epidemiology*

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Gastric cancer (GC) has a good prognosis, if detected at an early stage. The intestinal subtype of GC follows a stepwise progression to carcinoma, which is treatable with early detection and intervention using high-quality endoscopy. Premalignant lesions and gastric epithelial polyps are commonly encountered in clinical practice. Surveillance of patients with premalignant gastric lesions may aid in early diagnosis of GC, and thus improve chances of survival. An expert professional workgroup was formed to summarise the current evidence and provide recommendations on the management of patients with gastric premalignant lesions in Singapore. Twenty-five recommendations were made to address screening and surveillance, strategies for detection and management of gastric premalignant lesions, management of gastric epithelial polyps, and pathological reporting of gastric premalignant lesions.
Adenomatous Polyps ; Endoscopy ; Humans ; Precancerous Conditions/therapy* ; Singapore ; Stomach Neoplasms/therapy*

COVID-19 ; Heart Diseases ; Humans ; Incidence ; SARS-CoV-2 ; Singapore/epidemiology*

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Biopsy, Fine-Needle ; Diagnosis, Differential ; Facial Paralysis ; diagnosis ; etiology ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Parotid Gland ; pathology ; Sclerosis ; Sialadenitis ; diagnosis

Background: Eosinophilic gastroenteritis (EG) can mimic symptoms of common gastrointestinal (GI) disorders but responds well to appropriate treatment. Accurate diagnosis is central to effective management. Data on EG in Southeast Asia is lacking. We aim to describe the clinical profiles and treatment outcomes of adult patients with EG in a Singapore Tertiary Hospital. Materials and Methods: This retrospective study involved archival search of patients with GI biopsies that showed eosinophilic infiltration from January 2004 to December 2012. Patients’ clinical data from computerised hospital records and clinical notes was reviewed. Diagnostic criteria for EG included presence of GI symptoms with more than 30 eosinophils/high power field on GI biopsies. Patients with secondary causes for eosinophilia were excluded. Results: Eighteen patients with EG were identified (mean age 52 years; male/female: 11/7). Fifteen patients (83%) had peripheral blood eosinophilia. Seven patients (39%) had atopic conditions. Most common symptoms were diarrhoea and abdominal pain. Small intestine was the most common site involved. Endoscopic finding was non-specific. Ten patients were treated with corticosteroids (nine prednisolone, one budesonide): eight patients (89%) responded clinically to prednisolone but four patients (50%) relapsed following tapering-off of prednisolone and required maintenance dose. One patient each responded to diet elimination and montelukast respectively. Half of the remaining six patients who were treated with proton-pump inhibitors, antispasmodic or antidiarrheal agents still remained symptomatic. Conclusion: Prednisolone is an effective treatment though relapses are common. Small intestine is most commonly involved. EG should be considered in the evaluation of unexplained chronic recurrent GI symptoms.
Enteritis ; Gastroenteritis