BACKGROUND AND OBJECTIVE

The human nasal passages host major human pathogens. Recent research suggests that the microbial communities inhabiting the epithelial surfaces of the nasal passages play a key factor in maintaining a healthy microenvironment by affecting both resistance to pathogens and immunological responses. Colonization of the nasal cavity by different pathogens such as Staphylococcus aureus and Klebsiella aerogenes, is associated with a higher postoperative infection morbidity. Povidone-iodine (PVP-I) as an antiseptic has been proven to display high antibacterial, antiviral, and antifungal properties even at low concentrations, and was shown to be effective in the control of infections to limit their impact and spread. It can be used as a topical antiseptic for skin decontamination and wound management, as a nasal spray, or as a gargle. There are different methods in testing the efficacy of potential antimicrobial suspensions. This study aimed to determine the concentration of PVP-I that is most effective in nasal decolonization using microsuspension test and colorimetric minimum inhibitory concentration (MIC) determination assays, resazurin microtiter assay (REMA), and Dey-Engley (D/E) neutralizer assay. The findings of this study will contribute to knowledge regarding the intended use of PVP-I in microbial control, particularly in bacterial infections.

Several dilutions (2.0%, 1.0%, 0.5%, 0.25%, 0.1% and 0.09%) of commercially bought 10% (10 mg per 100 ml) povidone-iodine were prepared and tested against a standardized inoculum (1x105) of Staphylococcus aureus and Klebsiella aerogenes at different contacttimes (5 seconds, 10 seconds, 30 seconds, 1 minute, and 5 minutes). Microdilution suspension test was performed to determine the log reduction per variable, while REMA and D/E neutralizer assay were used to determine the MIC. A value of greater than or equal to 5 log reduction was considered effective for microdilution suspension test. Estimates of agreement statistics were used to interpret the results of the assay in which the overall percent agreement (OPA), positive percent agreement (PPA), negative percent agreement (NPA), and Cohen’s kappa statistics were calculated.

Povidone-iodine concentration of 0.25% exhibited ?5 log reduction against K. aerogenes at the minimum contact time of 5 seconds. On the other hand,  a slightly higher PVP-I concentration was required to achieve ?5 log reduction for S. aureus at 0.5% concentration and a minimum contact time of 1 minute. There was an observed concordance of the results of REMA and D/E neutralizer as MIC colorimetric indicators, which yielded an overall test percent agreement of 90.30% (95% CI: 84.73–94.36), and a strong level of agreement (? = 0.8, pCONCLUSION

Low povidone-iodine concentrations (i.e., 0.5% against S. aureus and 0.25% against K. aerogenes) were observed to have bactericidal activity of at least 5 log reduction as rapid as the minimum contact time of 5 seconds. Furthermore, D/E and REMA, as colorimetric indicators, had comparable performance (OPA = 90.30%; ? = 0.8, p
Human ; Bacteria ; Povidone-iodine ; Microbial Sensitivity Tests ; Anti-infective Agents, Local ; Enterobacter Aerogenes ; Staphylococcus Aureus

BACKGROUND

Down syndrome or trisomy 21, the most common chromosomal disorder, results from the presence of a third copy of chromosome 21 and manifests as mild to moderate intellectual disability, growth retardation, congenital heart defects, gastrointestinal abnormalities, and characteristic facial features. Several methods have been used to screen for Down syndrome in the prenatal period, such as ultrasound, biomarkers, cell-free DNA testing, and combinations of these tests. A positive result from one or more of these screening tests signals the need for confirmatory karyotyping to clinch the diagnosis. Ultrasound between 11 to 14 weeks of gestation can evaluate nuchal translucency (NT) to screen for Down syndrome. During the second trimester, a triple or quadruple test can also be performed alone or in addition to NT to quantify Down syndrome risk. In limited resource settings however, only the measurement of NT via ultrasound can be performed since biomarker tests are either unavailable or inaccessible. While the diagnostic performance of NT measurement alone has been investigated in several observational studies, there is no consensus on its performance as a sole test to screen for Down syndrome.

To determine the diagnostic performance of NT during prenatal first-trimester ultrasound as a screening test for Down syndrome.

We performed a systematic search on the PubMed, ProQuest, and Cochrane Library databases for recent systematic reviews and meta-analyses that addressed the objective. The existing reviews found were then independently appraised by the two reviewers with the AMSTAR-2 checklist. To update the existing reviews, a systematic search was done in the same databases to identify additional primary diagnostic studies, which were appraised using the QUADAS-2 tool. Random-effects univariate meta-analysis and summary receiving operator curve (HSROC) analysis for the outcomes were performed using Review Manager version 5.4 and R version 4.2.2, respectively. Subgroup analysis was performed by stratifying the baseline risk of mothers for fetal anomaly as low- or high-risk. Highrisk mothers were defined as women with risk factors such as advanced age, positive serum screen, presence of other ultrasound anomalies, and history of previous fetus with anomaly.

We found 22 cohort studies (n=225,846) of women at low-risk for fetal anomaly. The pooled sensitivity was 67.8% (95% CI: 61.4%-73.6%, I2=70.4%) and specificity was 96.3% (95% CI: 95.5%-96.9%, I2=96.7%). For low-risk women, the overall certainty of evidence was low, due to different modes of verification and heterogeneity not completely explained by variability in baseline risk or cut-points. Seven studies (n=9,197) were on high-risk women. The pooled sensitivity was 62.2% (95% CI: 54.1%-69.7%, I2=38.8%) and specificity was 96.5% (95% CI: 93.6%-98.1%, I2=95.5%). For women at high-risk, the evidence was rated as moderate due to differential verification.

Our analysis showed that NT measured through first-trimester ultrasound is specific for Down syndrome but has low sensitivity. Despite this, it is a useful screening test for Down syndrome in low-resource settings where other strategies may not be available or accessible. Furthermore, interpretation of NT results must take into consideration its limited sensitivity as this may lead to missed cases.

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography

INTRODUCTION: Radiology plays an integral role in fracture detection in the emergency department (ED). After hours, when there are fewer reporting radiologists, most radiographs are interpreted by ED physicians. A minority of these interpretations may miss diagnoses, which later require the callback of patients for further management. Artificial intelligence (AI) has been viewed as a potential solution to augment the shortage of radiologists after hours. We explored the efficacy of an AI solution in the detection of appendicular and pelvic fractures for adult radiographs performed after hours at a general hospital ED in Singapore, and estimated the potential monetary and non-monetary benefits. METHODS: One hundred and fifty anonymised abnormal radiographs were retrospectively collected and fed through an AI fracture detection solution. The radiographs were re-read by two radiologist reviewers and their consensus was established as the reference standard. Cases were stratified based on the concordance between the AI solution and the reviewers' findings. Discordant cases were further analysed based on the nature of the discrepancy into overcall and undercall subgroups. Statistical analysis was performed to evaluate the accuracy, sensitivity and inter-rater reliability of the AI solution. RESULTS: Ninety-two examinations were included in the final study radiograph set. The AI solution had a sensitivity of 98.9%, an accuracy of 85.9% and an almost perfect agreement with the reference standard. CONCLUSION An AI fracture detection solution has similar sensitivity to human radiologists in the detection of fractures on ED appendicular and pelvic radiographs. Its implementation offers significant potential measurable cost, manpower and time savings.
Humans ; Singapore ; Emergency Service, Hospital ; Fractures, Bone/diagnostic imaging* ; Artificial Intelligence ; Retrospective Studies ; Adult ; Male ; Female ; Cost Savings ; Middle Aged ; Pelvic Bones/diagnostic imaging* ; Reproducibility of Results ; Aged ; Sensitivity and Specificity ; Radiography

INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency department (ED) physicians on a binomial classification task. METHODS: A total of 1,314 paediatric elbow lateral radiographs (patient mean age 8.2 years) were retrospectively retrieved and classified based on annotation as normal or abnormal (with pathology). They were then randomly partitioned to a development set (993 images); first and second tuning (validation) sets (109 and 100 images, respectively); and a test set (112 images). An artificial intelligence (AI) model was trained on the development set using the EfficientNet B1 network architecture. Its performance on the test set was compared to that of five physicians (inter-rater agreement: fair). Performance of the AI model and the physician group was tested using McNemar test. RESULTS: The accuracy of the AI model on the test set was 80.4% (95% confidence interval [CI] 71.8%-87.3%), and the area under the receiver operating characteristic curve (AUROC) was 0.872 (95% CI 0.831-0.947). The performance of the AI model vs. the physician group on the test set was: sensitivity 79.0% (95% CI: 68.4%-89.5%) vs. 64.9% (95% CI: 52.5%-77.3%; P = 0.088); and specificity 81.8% (95% CI: 71.6%-92.0%) vs. 87.3% (95% CI: 78.5%-96.1%; P = 0.439). CONCLUSION The AI model showed good AUROC values and higher sensitivity, with the P-value at nominal significance when compared to the clinician group.
Humans ; Deep Learning ; Child ; Retrospective Studies ; Male ; Female ; Radiography/methods* ; ROC Curve ; Elbow/diagnostic imaging* ; Neural Networks, Computer ; Child, Preschool ; Elbow Joint/diagnostic imaging* ; Emergency Service, Hospital ; Adolescent ; Infant ; Artificial Intelligence

INTRODUCTION: The diagnosis of sarcopenia relies on key indicators such as handgrip strength, walking speed and muscle mass. Developing a composite index that integrates these measures could enhance clinical evaluation in older adults. This study aimed to standardise and combine these metrics to establish a z score for the sarcopenia composite index (ZoSCI) tailored for the ageing population. Additionally, we explore the risk factors associated with ZoSCI to provide insights into early prevention and intervention strategies. METHOD: This retrospective study analysed data between January 2017 and December 2021 from an elderly health programme in Taiwan, applying the Asian Working Group for Sarcopenia criteria to assess sarcopenia. ZoSCI was developed by standardising handgrip strength, walking speed and muscle mass into z scores and integrating them into a composite index. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values, and multiple regression analysis identified factors influencing ZoSCI. RESULTS: Among the 5047 participants, the prevalence of sarcopenia was 3.7%, lower than the reported global prevalence of 3.9-15.4%. ROC curve analysis established optimal cut-off points for distinguishing sarcopenia in ZoSCI: -1.85 (sensitivity 0.91, specificity 0.88) for males and -1.97 (sensitivity 0.93, specificity 0.88) for females. Factors associated with lower ZoSCI included advanced age, lower education levels, reduced exercise frequency, lower body mass index and creatinine levels. CONCLUSION This study introduces ZoSCI, a new compo-site quantitative indicator for identifying sarcopenia in older adults. The findings highlight specific risk factors that can inform early intervention. Future studies should validate ZoSCI globally, with international collaborations to ensure broader applicability.
Humans ; Sarcopenia/physiopathology* ; Male ; Aged ; Female ; Retrospective Studies ; Hand Strength ; Taiwan/epidemiology* ; ROC Curve ; Aged, 80 and over ; Risk Factors ; Walking Speed ; Geriatric Assessment/methods* ; Prevalence ; Muscle, Skeletal ; Middle Aged

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction. METHOD: Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI. RESULTS: There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV. CONCLUSION The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
Humans ; Male ; Female ; Emergency Service, Hospital ; Middle Aged ; Electrocardiography ; Machine Learning ; Singapore ; Chest Pain/blood* ; Troponin I/blood* ; Myocardial Infarction/blood* ; Risk Assessment/methods* ; Aged ; Algorithms ; Acute Coronary Syndrome/blood* ; Adult ; Sensitivity and Specificity

In this study, we employed a combination of genome mining and heteronuclear single quantum coherence (HSQC)-based small molecule accurate recognition technology (SMART) technology to search for fernane-type triterpenoids. Initially, potential endophytic fungi were identified through genome mining. Subsequently, fine fractions containing various fernane-type triterpenoids were selected using HSQC data collection and SMART prediction. These triterpenoids were then obtained through targeted isolation and identification. Finally, their antifungal activity was evaluated. As a result, three fernane-type triterpenoids, including two novel compounds, along with two new sesquiterpenes and four known compounds were isolated from one potential strain, Diaporthe discoidispora. Their structures were elucidated through analysis of high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) and nuclear magnetic resonance (NMR) spectroscopic data. The absolute configurations were determined using single-crystal X-ray diffraction analysis and electron capture detector (ECD) analysis. Compound 3 exhibited moderate antifungal activity against Candida albicans CMCC 98001 and Aspergillus niger.
Triterpenes/isolation & purification* ; Antifungal Agents/isolation & purification* ; Molecular Structure ; Candida albicans/drug effects* ; Ascomycota/genetics* ; Magnetic Resonance Spectroscopy ; Aspergillus niger/drug effects* ; Genome, Fungal ; Microbial Sensitivity Tests

Given the increasing concern regarding antibacterial resistance, the antimicrobial properties of naphthoquinones have recently attracted significant attention. While 1,4-naphthoquinone and its derivatives have been extensively studied, the antibacterial properties of 5,8-dihydroxy-1,4-naphthoquinone derivatives remain relatively unexplored. This study presents a comprehensive in vitro and in vivo analysis of the antibacterial activity of 35 naturally sourced and chemically synthesized derivatives of 5,8-dihydroxy-1,4-naphthoquinone. Kirby-Bauer antibiotic testing identified three compounds with activity against methicillin-resistant Staphylococcus aureus (MRSA), with one compound (PNP-02) demonstrating activity comparable to vancomycin in minimum inhibitory concentration, minimum bactericidal concentration (MBC), and time-kill assays. Microscopic and biochemical analyses revealed that PNP-02 adversely affects the cell wall and cell membrane of MRSA. Mechanistic investigations, including proteomic sequencing analyses, Western blotting, and RT-qPCR assays, indicated that PNP-02 compromises cell membrane integrity by inhibiting arginine biosynthesis and pyrimidine metabolism pathways, thereby increasing membrane permeability and inducing bacterial death. In an in vivo mouse model of skin wound healing, PNP-02 exhibited antibacterial efficacy similar to vancomycin. The compound demonstrated low toxicity to cultured human cells and in hemolysis assays and remained stable during serum incubation. These findings suggest that PNP-02 possesses promising bioactivity against MRSA and represents a potential novel antibacterial agent.
Methicillin-Resistant Staphylococcus aureus/genetics* ; Anti-Bacterial Agents/chemistry* ; Naphthoquinones/administration & dosage* ; Animals ; Microbial Sensitivity Tests ; Mice ; Humans ; Staphylococcal Infections/microbiology* ; Molecular Structure

This study aims to establish an antibody detection method for porcine deltacoronavirus (PDCoV). The recombinant proteins PDCoV-N1 and PDCoV-N2 were expressed via the prokaryotic plasmid pColdII harboring the N gene sequence of the PDCoV strain CH/XJYN/2016. The reactivity and specificity of PDCoV-N1 and PDCoV-N2 with anti-PEDV sera were analyzed after the recombinant proteins were analyzed by SDS-PAGE and purified by the Ni-NTA Superflow Cartridge. Meanwhile, Western blotting and indirect immunofluorescence assay were carried out separately to validate the recombinant proteins PDCoV-N1 and PDCoV-N2. Finally, we established an indirect ELISA method based on the recombinant protein PDCoV-N2 after optimizing the conditions and tested the sensitivity, specificity, and reproducibility of the method. Then, the established method was employed to examine 102 clinical serum samples. The recombinant protein PDCoV-N2 showed low cross-reactivity with anti-PEDV sera. The optimal conditions of the indirect ELISA method based on PDCoV-N2 were as follows: the antigen coating concentration of 1.25 μg/mL and coating at 37 ℃ for 1 h; blocking by BSA overnight at 4 ℃; serum sample dilution at 1:50 and incubation at 37 ℃ for 1 h; secondary antibody dilution at 1:80 000 and incubation at 37 ℃ for 1 h; color development with TMB chromogenic solution at 37 ℃ for 10 min. The S/P value ≥ 0.45, ≤0.38, and between 0.45 and 0.38 indicated that the test sample was positive, negative, and suspicious, respectively. The testing results of the antisera against porcine epidemic diarrhea virus (PEDV), porcine circovirus 2 (PCV2), transmissible gastroenteritis virus (TGEV), foot-and-mouth disease virus (FMDV), and African swine fever virus (ASFV) showed that the S/P values were all less than 0.38. The testing results of the 800-fold diluted anti-PDCoV sera were still positive. The results of the inter- and intra-batch tests showed that the coefficients of variation of this method were less than 10%. Clinical serum sample test results showed the coincidence rate between this method and neutralization test was 94.12%. In this study, an ELISA method for the detection of anti-PDCoV antibodies was successfully established based on the truncated N protein of PDCoV. This method is sensitive, specific, stable, and reproducible, serving as a new method for the clinical diagnosis of PDCoV.
Animals ; Enzyme-Linked Immunosorbent Assay/methods* ; Swine ; Antibodies, Viral/blood* ; Recombinant Proteins/genetics* ; Deltacoronavirus/isolation & purification* ; Coronavirus Infections/virology* ; Swine Diseases/diagnosis* ; Coronavirus Nucleocapsid Proteins ; Sensitivity and Specificity