BackgroundDiagnosing childhood urinary tract infections (UTI) is challenging.AimValidate clinical prediction rules (UTIcalc, DUTY, Gorelick) for paediatric UTIs in primary care.Design & settingPost-hoc analysis of a cross-sectional study in 39 general practices and 2 emergency departments (Belgium, March 2019 to March 2020).MethodPhysicians recruited acutely ill children ≤18 years and sampled urine systematically for culture. Per rule, we performed an apparent validation; calculated sensitivities and specificities with 95%CI per threshold in the target group. For the DUTY coefficient-based algorithm, we performed a logistic calibration and calculated the Area Under the Curve with 95%CI.ResultsOf 834 children ≤18 years recruited, there were 297 children <5 years. The UTIcalc and Gorelick score had high to moderate sensitivity and low specificity (UTIcalc ≥2%) 75%; and 16% respectively; Gorelick (≥2 variables) 91%; and 8%. In contrast, the DUTY score ≥5 points had low sensitivity (8%), but high specificity (99%). Urine samples would be obtained in 72% vs 38% (UTIcalc), 92% vs 38% (Gorelick) or 1% vs 32% (DUTY) of children, compared to routine care. The number of missed infections per score was 1/4 (UTIcalc), 2/23 (Gorelick) and 24/26 (DUTY). The UTIcalc+ dipstick model had high sensitivity and specificity (100%; and 91%); resulting in no missed cases and 59% (95%CI 49%–68%) of antibiotics prescribed inappropriately.ConclusionIn this study, the UTIcalc and Gorelick score were useful for ruling out UTI but resulted in high urine sampling rates. The DUTY score had low sensitivity, meaning that 92% of UTIs would be missed.
UPK2 exhibits excellent specificity for urothelial carcinoma (UC). UPK2 evaluation can be useful in making the correct diagnosis of UC. However, UPK2 detection by immunohistochemistry (IHC) has relatively low sensitivity. This paper aimed to compare the diagnostic sensitivity of RNAscope and IHC for evaluation of the UPK2 status in UC.
Tissue blocks from 127 conventional bladder UCs, 45 variant bladder UCs, 24 upper tract UCs and 23 metastatic UCs were selected for this study. IHC and RNAscope were used to detect the UPK2 status in UCs. Then, comparisons of the two methods were undertaken.
There was no significant difference between RNAscope and IHC for the evaluation of the UPK2 positivity rate in UC (68.0% vs. 62.6%, P = 0.141). Correlation analysis revealed a moderate positive correlation for detection of UPK2: RNAscope vs. IHC (P < 0.001, R = 0.441). Our results showed a trend toward a higher positive UPK2 rate detected by RNAscope (53.3%) than by IHC (35.6%) in variant bladder UCs. Disappointingly, the P value did not indicate a significant difference (P = 0.057).
RNAscope for UPK2 appeared to perform similarly to IHC, with a marginally higher positive rate, suggesting it could be used as an alternative or adjunct to UPK2 IHC.
Paper-based analytical devices (PADs), including lateral flow assays (LFAs), dipstick assays and microfluidic PADs (μPADs), have a great impact on the healthcare realm and environmental monitoring. This is especially evident in developing countries because PADs-based point-of-care testing (POCT) enables to rapidly determine various (bio)chemical analytes in a miniaturized, cost-effective and user-friendly manner. Low sensitivity and poor specificity are the main bottlenecks associated with PADs, which limit the entry of PADs into the real-life applications. The application of nanomaterials in PADs is showing great improvement in their detection performance in terms of sensitivity, selectivity and accuracy since the nanomaterials have unique physicochemical properties. In this review, the research progress on the nanomaterial-based PADs is summarized by highlighting representative recent publications. We mainly focus on the detection principles, the sensing mechanisms of how they work and applications in disease diagnosis, environmental monitoring and food safety management. In addition, the limitations and challenges associated with the development of nanomaterial-based PADs are discussed, and further directions in this research field are proposed.
Aiming at the fact that traditional convolutional neural networks cannot effectively extract signal features in complex application scenarios, a sleep apnea (SA) detection method based on multi-scale residual networks is proposed. First, we analyze the physiological mechanism of SA, which uses the RR interval signals and R peak signals derived from the ECG signals as input. Then, a multi-scale residual network is used to extract the characteristics of the original signals in order to obtain sensitive characteristics from various angles. Because the residual structure is used in the model, the problem of model degradation can be avoided. Finally, a fully connected layer is introduced for SA detection. In order to overcome the impact of class imbalance, a focal loss function is introduced to replace the traditional cross-entropy loss function, which makes the model pay more attention to learning difficult samples in the training phase. Experimental results from the Apnea-ECG dataset show that the accuracy, sensitivity and specificity of the proposed multi-scale residual network are 86.0%, 84.1% and 87.1%, respectively. These results indicate that the proposed method not only achieves greater recognition accuracy than other methods, but it also effectively resolves the problem of low sensitivity caused by class imbalance.
Measuring pH has become a major key for determining health conditions, and food safety. The traditional pH assessment approaches are costly and offer low sensitivity. Here, a novel pH sensor based on a pH-responsive hydrogel has been developed. A Fresnel lens pattern was replicated on the surface of the pH-responsive hydrogel using the replica mould method. The pH sensors were tested in a pH range of 4–7. Introducing various pH solutions to the pH sensor led to volumetric shifts as the hydrogel swelled with pH. Consequently, the dimensions of the replicated Fresnel lens changed, modifying the focal length and the focus efficiency of the optical sensor. As a result, the measured optical power at a fixed distance from the sensor changed with pH. The optical sensor showed the best performance in the acidic region when pH changed from 4.5 to 5.5, in which the recorded power increased by 13%. The sensor exhibited high sensitivity to pH changes with a short respond time in a reversible manner. The developed pH optical sensor may have applications in medical point-of-care diagnostics and wearable continuous pH detection devices.
BackgroundCulture-based microbiological investigation of hospital-acquired or ventilator-associated pneumonia (HAP or VAP) is insensitive, with aetiological agents often unidentified. This can lead to excess antimicrobial treatment of patients with susceptible pathogens, while those with resistant bacteria are treated inadequately for prolonged periods. Using PCR to seek pathogens and their resistance genes directly from clinical samples may improve therapy and stewardship.MethodsSurplus routine lower respiratory tract samples were collected from intensive care unit patients about to receive new or changed antibiotics for hospital-onset lower respiratory tract infections at 15 UK hospitals. Testing was performed using the BioFire FilmArray Pneumonia Panel (bioMérieux) and Unyvero Pneumonia Panel (Curetis). Concordance analysis compared machine and routine microbiology results, while Bayesian latent class (BLC) analysis estimated the sensitivity and specificity of each test, incorporating information from both PCR panels and routine microbiology.FindingsIn 652 eligible samples; PCR identified pathogens in considerably more samples compared with routine microbiology: 60.4% and 74.2% for Unyvero and FilmArray respectively vs 44.2% by routine microbiology. PCR tests also detected more pathogens per sample than routine microbiology. For common HAP/VAP pathogens, FilmArray had sensitivity of 91.7%–100.0% and specificity of 87.5%–99.5%; Unyvero had sensitivity of 50.0%–100.0%%, and specificity of 89.4%–99.0%. BLC analysis indicated that, compared with PCR, routine microbiology had low sensitivity, ranging from 27.0% to 69.4%.InterpretationConventional and BLC analysis demonstrated that both platforms performed similarly and were considerably more sensitive than routine microbiology, detecting potential pathogens in patient samples reported as culture negative. The increased sensitivity of detection realised by PCR offers potential for improved antimicrobial prescribing.
Objective: We aimed to apply the modified systemic inflammatory response syndrome (mSIRS), the quick sequential organ failure assessment score (qSOFA), and National Early Warning Score (NEWS) to triage suspected sepsis patients. Therefore, knowing the predictive performance of each scoring system, using given cut-points for triaging patients with suspected sepsis, could help predict the progression of sepsis.Material and Methods: This study is a single-center retrospective chart review. The study enrolled patients older than 18 years with suspected sepsis patient at the time they presented at the triage zone. The primary outcome was to determine which scoring system were the most accurate to triage sepsis patients. The secondary outcomes were predictions of mortality related to the scoring.Results: Considering the outcome to be represented by a SOFA score of ≥2, the area under the curve of the receiver operating characteristic curves for the entire range of mSIRS, qSOFA and NEWS were 0.494, 0.669 and 0.751, respectively. Using a cut point for qSOFA of ≥2 provided a low sensitivity of 36.2% and high specificity of 93.0%; whereas, using a cut point for NEWS of >4 provided a high sensitivity of 89.0% and low specificity of 33.0%.Conclusion: In summary, qSOFA is the most accurate scoring system for diagnosis sepsis which was consistent with previous study. However, qSOFA had the lowest sensitivity, so is not appropriate in a triage situation. Therefore, we decided to use NEWS as the triage tool because of its better sensitivity and acceptable specificity as we need to triage almost all possible cases.
As the most common serious disaster, fire may cause a lot of damages. Early detection and treatment of fires are of great significance to ensure public safety and to reduce losses caused by fires. However, traditional fire detectors are facing some focus issues such as low sensitivity and limited detection scenes. To overcome these problems, a video fire detection hybrid method based on random forest (RF) feature selection and back propagation (BP) neural network is proposed. The improved flame color model in RGB and HSI space and the visual background extractor (ViBe) in moving target detection algorithm are used to segment the suspected flame regions. Then, multidimensional features of flames are extracted from the suspected regions, and these extracted features are combined and selected according to the RF feature importance analysis. Finally, a BP neural network model is constructed for multifeature fusion and fire recognition. The test results on several experimental video sets show that the proposed method can effectively avoid feature interference and has an excellent recognition effect on fires in a variety of scenarios. The proposed method is applicable for fire recognition applied in video surveillance and detection robots.
Autism screening is recommended at 18- and 24-month pediatric well visits. The Modified Checklist for Autism in Toddlers—Revised (M-CHAT-R) authors recommend a follow-up interview (M-CHAT-R/F) when positive. M-CHAT-R/F may be less accurate for 18-month-olds than 24-month-olds and accuracy for identification prior to two years is not known in samples that include children screening negative. Since autism symptoms may emerge gradually, ordinally scoring items based on the full range of response options, such as in the 10-item version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), might better capture autism signs than the dichotomous (i.e., yes/no) items in M-CHAT-R or the pass/fail scoring of Q-CHAT-10 items. The aims of this study were to determine and compare the accuracy of the M-CHAT-R/F and the Q-CHAT-10 and to describe the accuracy of the ordinally scored Q-CHAT-10 (Q-CHAT-10-O) for predicting autism in a sample of children who were screened at 18 months.
This is a community pediatrics validation study with screen positive (n = 167) and age- and practice-matched screen negative children (n = 241) recruited for diagnostic evaluations completed prior to 2 years old.
Clinical diagnosis of autism was based on results of in-person diagnostic autism evaluations by research reliable testers blind to screening results and using the Autism Diagnostic Observation Schedule—Second Edition (ADOS-2) Toddler Module and Mullen Scales of Early Learning (MSEL) per standard guidelines.
While the M-CHAT-R/F had higher specificity and PPV compared to M-CHAT-R, Q-CHAT-10-O showed higher sensitivity than M-CHAT-R/F and Q-CHAT-10.
Many parents declined participation and the sample is over-represented by higher educated parents. Results cannot be extended to older ages.
Limitations of the currently recommended two-stage M-CHAT-R/F at the 18-month visit include low sensitivity with minimal balancing benefit of improved PPV from the follow-up interview. Ordinal, rather than dichotomous, scoring of autism screening items appears to be beneficial at this age. The Q-CHAT-10-O with ordinal scoring shows advantages to M-CHAT-R/F with half the number of items, no requirement for a follow-up interview, and improved sensitivity. Yet, Q-CHAT-10-O sensitivity is less than M-CHAT-R (without follow-up) and specificity is less than the two-stage procedure. Such limitations are consistent with recognition that screening needs to recur beyond this age.
Objectives We examined the effect of sarcopenia on early surgical outcomes in patients with critical limb ischemia (CLI) in terms of major adverse cardiac events (MACE) and major adverse limb events (MALE), as well as the value of inflammatory markers of neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte ratios (PLR) as indicators of sarcopenia in CLI patients. Methods This was an observational retrospective single-center study. Patients who required surgical revascularization for CLI between October 2015 and December 2020 were identified. Psoas muscle areas were calculated from computed tomography images for psoas muscle index (PMI) calculations. Sarcopenia was defined as PMI < 5.5 cm2/m2 for men and PMI < 4.0 cm2/m2 for women. Risk factors for 30-day major adverse cardiac events (MACE) and major adverse limb events (MALE) were analyzed. NLR and PLR were compared between sarcopenic and non-sarcopenic patients. Results The mean age of 217 study patients was 61.5 ± 10.9, and 16 (7.4%) patients were female. 82 (37.8%) patients were sarcopenic. Patients with sarcopenia were older (65.1 ± 9.3 vs 59.4 ± 11.2, p < .001) and history of myocardial infarction was more frequent (23.2% vs 12.6%, p = 0.042) among sarcopenic patients. Sarcopenic patients more frequently encountered MACE (9.8% vs 0.7%, p = 0.002), but not MALE. Sarcopenia increased early postoperative MACE in our cohort with an odds ratio of 11.925. NLR was not different between the two groups, while PLR was higher (127.16 vs 104.06, p = 0.010) among sarcopenic patients. The platelet-to-lymphocyte ratio of 125.11 had a sensitivity of 53.7% and a specificity of 68.1% for differentiating sarcopenia. Conclusions Sarcopenia was associated with more frequent 30-day MACE and perioperative mortality after revascularization for CLI. 30-day MALE was not increased in patients with sarcopenia. The use of PLR as a simple marker of sarcopenia is limited by its low sensitivity and specificity.