Synoptic Reporting in Clinical Placental Pathology: A Preliminary Investigation Into Report Findings and Interobserver Agreement.

INTRODUCTION: Placental pathology is key for investigating adverse pregnancy outcomes, however, lack of standardization in reporting has limited clinical utility. We evaluated a novel placental pathology synoptic report, comparing its robustness to narrative reports, and assessed interobserver agreement. METHODS: 100 singleton placentas were included. Histology slides were examined by 2 senior perinatal pathologists and 2 pathology residents using a synoptic report (32 lesions). Historical narrative reports were compared to synoptic reports. Kappa scores were calculated for interobserver agreement between senior, resident, and senior vs resident pathologists. RESULTS: Synoptic reporting detected 169 (51.4%) lesion instances initially not included in historical reports. Amongst senior pathologists, 64% of all lesions examined demonstrated fair-to-excellent agreement (Kappa ≥0.41), with only 26% of Kappas ≥0.41 amongst those examined by resident pathologists. Well-characterized lesions (e.g., chorioamnionitis) demonstrated higher agreement, with lower agreement for uncommon lesions and those previously shown to have poor consensus. DISCUSSION: Synoptic reporting is one proposed method to address issues in placenta pathology reporting. The synoptic report generally identifies more lesions compared to the narrative report, however clinical significance remains unclear. Interobserver agreement is likely related to differential in experience. Further efforts to improve overall standardization of placenta pathology reporting are needed.

Feasibility Study of "Snuffbox" Radial Access for Visceral Interventions.

"Snuffbox" radial access entails sheath insertion into the dorsal branch of the radial artery within the so-called anatomic snuffbox. The purpose of this report is to describe the technique and early experience in 50 visceral interventional procedures performed in 31 patients, which included liver embolotherapy, visceral arterial stent insertion, aneurysm embolization, and emergency embolization. In all cases, the procedures were successfully completed by using the snuffbox access, with a single case of asymptomatic pseudoaneurysm as the only access-related complication. Early experience showed that snuffbox radial access is technically feasible and represents a viable alternative to conventional radial access for visceral intervention procedures.

Prevention of Relapse and Recurrence in Adults with Major Depressive Disorder: Systematic Review and Meta-Analyses of Controlled Trials.

BACKGROUND: Findings of substantial remaining morbidity in treated major depressive disorder (MDD) led us to review controlled trials of treatments aimed at preventing early relapses or later recurrences in adults diagnosed with MDD to summarize available data and to guide further research. METHODS: Reports (n = 97) were identified through systematic, computerized literature searching up to February 2015. Treatment versus control outcomes were summarized by random-effects meta-analyses. RESULTS: In 45 reports of 72 trials (n = 14 450 subjects) lasting 33.4 weeks, antidepressants were more effective than placebos in preventing relapses (response rates [RR] = 1.90, confidence interval [CI]: 1.73-2.08; NNT = 4.4; p < 0.0001). In 35 reports of 37 trials (n = 7253) lasting 27.0 months, antidepressants were effective in preventing recurrences (RR = 2.03, CI 1.80-2.28; NNT = 3.8; p < 0.0001), with minor differences among drug types. In 17 reports of 22 trials (n = 1 969) lasting 23.7 months, psychosocial interventions yielded inconsistent or inconclusive results. CONCLUSIONS: Despite evidence of the efficacy of drug treatment compared to placebos or other controls, the findings further underscore the substantial, unresolved morbidity in treated MDD patients and strongly encourage further evaluations of specific, improved individual and combination therapies (pharmacological and psychological) conducted over longer times, as well as identifying clinical predictors of positive or unfavorable responses and of intolerability of long-term treatments in MDD.

Duration of initial antidepressant treatment and subsequent relapse of major depression.

BACKGROUND: The efficacy, limitations, and methods of studying antidepressant treatment continued beyond initial weeks of acute major depression remain incompletely resolved. AIMS: For subjects treated in controlled trials for acute depression, we analyzed the relationship of relapse risk within 12 months of rerandomizing to placebo versus duration of initial treatment and putative stabilization. METHODS: With data from placebo arms of 45 relevant controlled trials identified in recent, systematic reviews were pooled and analyzed by regression modeling. RESULTS: There was a strong inverse correlation of shorter initial treatment and greater relapse risk after rerandomizing to placebo treatment, best fit to a power function (P ≤ 0.003); relapse risk differed by 11.4-fold, declining sharply as initial treatment continued for 16 to 20 weeks or more. CONCLUSIONS: Discontinuation of antidepressant treatment for major depressive episodes at times less than 6 months was associated with rising risks after randomization to continuation with placebo. This relationship requires critical consideration in both clinical management of depressed patients and the design and interpretation of treatment discontinuation trials.

Utility of ISARIC 4C Mortality Score, Vaccination History, and Anti-S Antibody Titre in Predicting Risk of Severe COVID-19.

The ISARIC 4C Mortality score was developed to predict mortality risk among patients with COVID-19. Its performance among vaccinated individuals is understudied. This is a retrospective study of all patients with SARS-CoV-2 infection admitted to the National Centre for Infectious Diseases, Singapore, from January-2020 to December-2021. Demographic, clinical, and laboratory data were extracted, and multiple logistic regression (MLR) models were developed to predict the relationship between ISARIC score, vaccination status, anti-S antibody titre, and severe COVID-19. A total of 6377 patients were identified, of which 5329 met the study eligibility criteria. The median age of the patients was 47 years (IQR 35-71), 1264 (23.7%) were female, and 1239 (25.7%) were vaccinated. Severe disease occurred in 499 (9.4%) patients, including 133 (2.5%) deaths. After stratification, 3.0% of patients with low (0-4), 17.8% of patients with moderate (5-9), and 36.2% of patients with high (≥10) ISARIC scores developed severe COVID-19. Vaccination was associated with a reduced risk of progression to severe COVID-19 in the MLR model: aOR 0.88 (95% CI: 0.86-0.90), and the risk of severe COVID-19 decreased inversely to anti-S antibody titres. The anti-S antibody titre should be further investigated as an adjunct to the ISARIC score to triage COVID-19 patients for hospital admission and antiviral therapy.

Machine learning in medicine: what clinicians should know.

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.

Harnessing artificial intelligence in radiology to augment population health.

This review article serves to highlight radiological services as a major cost driver for the healthcare sector, and the potential improvements in productivity and cost savings that can be generated by incorporating artificial intelligence (AI) into the radiology workflow, referencing Singapore healthcare as an example. More specifically, we will discuss the opportunities for AI in lowering healthcare costs and supporting transformational shifts in our care model in the following domains: predictive analytics for optimising throughput and appropriate referrals, computer vision for image enhancement (to increase scanner efficiency and decrease radiation exposure) and pattern recognition (to aid human interpretation and worklist prioritisation), natural language processing and large language models for optimising reports and text data-mining. In the context of preventive health, we will discuss how AI can support population level screening for major disease burdens through opportunistic screening and democratise expertise to increase access to radiological services in primary and community care.

Efficacy of texture analysis of pre-operative magnetic resonance imaging in predicting microvascular invasion in hepatocellular carcinoma.

BACKGROUND: Presence of microvascular invasion (MVI) indicates poorer prognosis post-curative resection of hepatocellular carcinoma (HCC), with an increased chance of tumour recurrence. By present standards, MVI can only be diagnosed post-operatively on histopathology. Texture analysis potentially allows identification of patients who are considered 'high risk' through analysis of pre-operative magnetic resonance imaging (MRI) studies. This will allow for better patient selection, improved individualised therapy (such as extended surgical margins or adjuvant therapy) and pre-operative prognostication. AIM: This study aims to evaluate the accuracy of texture analysis on pre-operative MRI in predicting MVI in HCC. METHODS: Retrospective review of patients with new cases of HCC who underwent hepatectomy between 2007 and 2015 was performed. Exclusion criteria: No pre-operative MRI, significant movement artefacts, loss-to-follow-up, ruptured HCCs, previous hepatectomy and adjuvant therapy. Fifty patients were divided into MVI (n = 15) and non-MVI (n = 35) groups based on tumour histology. Selected images of the tumour on post-contrast-enhanced T1-weighted MRI were analysed. Both qualitative (performed by radiologists) and quantitative data (performed by software) were obtained. Radiomics texture parameters were extracted based on the largest cross-sectional area of each tumor and analysed using MaZda software. Five separate methods were performed. Methods 1, 2 and 3 exclusively made use of features derived from arterial, portovenous and equilibrium phases respectively. Methods 4 and 5 made use of the comparatively significant features to attain optimal performance. RESULTS: Method 5 achieved the highest accuracy of 87.8% with sensitivity of 73% and specificity of 94%. CONCLUSION: Texture analysis of tumours on pre-operative MRI can predict presence of MVI in HCC with accuracies of up to 87.8% and can potentially impact clinical management.

Hmgcs2-mediated ketogenesis modulates high-fat diet-induced hepatosteatosis.

OBJECTIVE: Aberrant ketogenesis is correlated with the degree of steatosis in non-alcoholic fatty liver disease (NAFLD) patients, and an inborn error of ketogenesis (mitochondrial HMG-CoA synthase deficiency) is commonly associated with the development of the fatty liver. Here we aimed to determine the impact of Hmgcs2-mediated ketogenesis and its modulations on the development and treatment of fatty liver disease. METHODS: Loss- and gain-of-ketogenic function models, achieved by Hmgcs2 knockout and overexpression, respectively, were utilized to investigate the role of ketogenesis in the hepatic lipid accumulation during postnatal development and in a high-fat diet-induced NAFLD mouse model. RESULTS: Ketogenic function was decreased in NAFLD mice with a reduction in Hmgcs2 expression. Mice lacking Hmgcs2 developed spontaneous fatty liver phenotype during postnatal development, which was rescued by a shift to a low-fat dietary composition via early weaning. Hmgcs2 heterozygous adult mice, which exhibited lower ketogenic activity, were more susceptible to diet-induced NAFLD development, whereas HMGCS2 overexpression in NAFLD mice improved hepatosteatosis and glucose homeostasis. CONCLUSIONS: Our study adds new knowledge to the field of ketone body metabolism and shows that Hmgcs2-mediated ketogenesis modulates hepatic lipid regulation under a fat-enriched nutritional environment. The regulation of hepatic ketogenesis may be a viable therapeutic strategy in the prevention and treatment of hepatosteatosis.

Deep Supervised Domain Adaptation for Pneumonia Diagnosis From Chest X-Ray Images.

Pneumonia is one of the most common treatable causes of death, and early diagnosis allows for early intervention. Automated diagnosis of pneumonia can therefore improve outcomes. However, it is challenging to develop high-performance deep learning models due to the lack of well-annotated data for training. This paper proposes a novel method, called Deep Supervised Domain Adaptation (DSDA), to automatically diagnose pneumonia from chest X-ray images. Specifically, we propose to transfer the knowledge from a publicly available large-scale source dataset (ChestX-ray14) to a well-annotated but small-scale target dataset (the TTSH dataset). DSDA aligns the distributions of the source domain and the target domain according to the underlying semantics of the training samples. It includes two task-specific sub-networks for the source domain and the target domain, respectively. These two sub-networks share the feature extraction layers and are trained in an end-to-end manner. Unlike most existing domain adaptation approaches that perform the same tasks in the source domain and the target domain, we attempt to transfer the knowledge from a multi-label classification task in the source domain to a binary classification task in the target domain. To evaluate the effectiveness of our method, we compare it with several existing peer methods. The experimental results show that our method can achieve promising performance for automated pneumonia diagnosis.

Diagnostic Performance of a Deep Learning Model Deployed at a National COVID-19 Screening Facility for Detection of Pneumonia on Frontal Chest Radiographs.

(1) Background: Chest radiographs are the mainstay of initial radiological investigation in this COVID-19 pandemic. A reliable and readily deployable artificial intelligence (AI) algorithm that detects pneumonia in COVID-19 suspects can be useful for screening or triage in a hospital setting. This study has a few objectives: first, to develop a model that accurately detects pneumonia in COVID-19 suspects; second, to assess its performance in a real-world clinical setting; and third, by integrating the model with the daily clinical workflow, to measure its impact on report turn-around time. (2) Methods: The model was developed from the NIH Chest-14 open-source dataset and fine-tuned using an internal dataset comprising more than 4000 CXRs acquired in our institution. Input from two senior radiologists provided the reference standard. The model was integrated into daily clinical workflow, prioritising abnormal CXRs for expedited reporting. Area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, and specificity were calculated to characterise diagnostic performance. The average time taken by radiologists in reporting the CXRs was compared against the mean baseline time taken prior to implementation of the AI model. (3) Results: 9431 unique CXRs were included in the datasets, of which 1232 were ground truth-labelled positive for pneumonia. On the "live" dataset, the model achieved an AUC of 0.95 (95% confidence interval (CI): 0.92, 0.96) corresponding to a specificity of 97% (95% CI: 0.97, 0.98) and sensitivity of 79% (95% CI: 0.72, 0.84). No statistically significant degradation of diagnostic performance was encountered during clinical deployment, and report turn-around time was reduced by 22%. (4) Conclusion: In real-world clinical deployment, our model expedites reporting of pneumonia in COVID-19 suspects while preserving diagnostic performance without significant model drift.

Positive modulation of mGluR5 attenuates seizures and reduces TNF-α+ macrophages and microglia in the brain in a murine model of virus-induced temporal lobe epilepsy.

Viral encephalitis markedly increases the risk for the development of epilepsy. The Theiler's murine encephalomyelitis virus (TMEV)-induced model of seizures/epilepsy is a murine model of both viral-induced seizures/epilepsy and human Temporal Lobe Epilepsy. The inflammatory cytokines interleukin (IL)-6 and tumor necrosis factor (TNF)-α have been shown to play a role in seizure development in the TMEV-induced model of seizures/epilepsy, and infiltrating macrophages along with microglia have been shown to be major producers of these cytokines. The metabotropic glutamate receptor 5 (mGluR5) is a G-protein coupled receptor that has been shown to reduce IL-6 and TNF-α and to provide neuroprotection in other disease models. Therefore, we hypothesized that stimulation of mGluR5 would not only reduce seizures but attenuate IL-6 and TNF-α production in microglia and macrophages in the TMEV model. We found that pharmacological stimulation of mGluR5 with the selective positive allosteric modulator VU0360172 not only reduced acute seizure outcomes, but also reduced the percent of microglia and macrophages producing TNF-α 3 days post infection. Furthermore, treatment with VU0360172 did not alter the level of viral antigen, compared to controls, showing that this treatment does not compromise viral clearance. These results establish that mGluR5 may represent a therapeutic target in the TMEV-induced model of seizures/epilepsy.

Evaluation of tumor morphologies and association with biochemical recurrence after radical prostatectomy in grade group 5 prostate cancer.

We assessed Gleason pattern 5 (GP5) and other prostatic adenocarcinoma (PCa) morphologies to determine their association with biochemical recurrence (BCR). A search for grade group 5 PCa with radical prostatectomy (RP) yielded 49 patients. RPs were reviewed for %GP5 and morphologies (sheets, single cells, cords, small solid cylinders, solid medium to large nests with rosette-like spaces [SMLNRS], comedonecrosis, cribriform glands, glomerulations, intraductal carcinoma of the prostate [IDC-P], and prostatic ductal adenocarcinoma [PDCa]). Prevalence of morphologies was as follows: single cells 100%, cribriform glands 98.7%, cords 85.7%, IDC-P 77.6%, comedonecrosis 53.1%, sheets 49.0%, small solid cylinders 49.0%, PDCa 44.9%, glomerulations 34.7%, and SMLNRS 14.3%. From 28 patients who were treated with RP as monotherapy, 64.3% (18/28) had BCR. Comedonecrosis, sheets, small solid cylinders, IDC-P, and PDCa were significantly associated with BCR. Number of morphologies on RP and %GP5 were higher in patients with BCR (6.8 ± 2.1 versus 3.7 ± 2.9%; P < 0.001 and 26.9 ± 16.8 versus 11.4 ± 14.1%; P = 0.02) with area under ROC curve of 0.89 (confidence intervals [CI] 0.77-1.00). Sensitivity/specificity was 77.8/80.0% for predicting BCR when ≥ 5 morphologies were present and 0.79 (CI 0.60-0.99) with sensitivity/specificity of 66.7/80.0% for predicting BCR when ≥ 15% GP5 was present. Hazard ratio for BCR was higher with increasing number of morphologies (1.23, CI 1.02-1.49; P = 0.034) but not %GP5 (0.99, CI 0.97-1.02, P = 0.622). Our results indicate that GP5 morphologies may represent a biologically heterogeneous group and that an increasing number of PCa morphologies on RP is strongly associated with an increased risk of BCR.