Long-term effects of establishing outpatient pooling funds on financial protection: Empirical evidence from a quasi-natural experiment in China.

BACKGROUND: A limited benefit package for outpatient care in Chinese universal health coverage led to high out-of-pocket outpatient payments, and even medical impoverishment. The outpatient pooling fund model was introduced in China's Urban Employee Basic Medical Insurance to reduce cost-sharing for outpatient care. This study attempts to examine the dynamic effects of the outpatient pooling scheme on financial risk protection for its enrollees. METHODS: A total of 18,097 individual-level observations covering 52 prefectures were extracted from six waves of China Health and Nutrition Survey (2000-2015). The difference-in-differences model with multiple periods and event study were employed to investigate the dynamic effects of reform on catastrophic health expenditure (CHE) and impoverishing health expenditure (IHE) and potential mechanisms. RESULTS: Our results showed outpatient pooling scheme generated a significant effect on reducing the probability of incurring CHE (ß = -0.004, 95% CI = -0.009 to -0.006) and IHE (ß = -0.007, 95% CI = -0.012 to -0.001), especially for elderly people over 60 years old. The realization of this effect may depend on the reduction of outpatient cost-sharing, increased outpatient care utilization, as well as decreased inpatient care utilization after reform. However, event study found the effectiveness of outpatient pooling reducing CHE and IHE occurrences appeared to be weak even insignificant in more recent years relative to the initial years of policy implementation. CONCLUSIONS: Establishing an outpatient pooling system is effective to alleviate the financial risk caused by health expenditures in China. Optimising health service delivery aimed at enhancing health insurance purchasing efficiency are deemed imperative for sustaining the policy effectiveness.

Wildlife target detection based on improved YOLOX-s network.

To addresse the problem of poor detection accuracy or even false detection of wildlife caused by rainy environment at night. In this paper, a wildlife target detection algorithm based on improved YOLOX-s network is proposed. Our algorithm comprises the MobileViT-Pooling module, the Dynamic Head module, and the Focal-IoU module.First, the MobileViT-Pooling module is introduced.It is based on the MobileViT attention mechanism, which uses a spatial pooling operator with no parameters as a token mixer module to reduce the number of network parameters. This module performs feature extraction on three feature layers of the backbone network output respectively, senses the global information and strengthens the weight of the effective information. Second, the Dynamic Head module is used on the downstream task of network detection, which fuses the information of scale sensing, spatial sensing, and task sensing and improves the representation ability of the target detection head. Lastly, the Focal idea is utilized to improve the IoU loss function, which balances the learning of high and low quality IoU for the network. Experimental results reveal that our algorithm achieves a notable performance boost with mAP@0.5 reaching 87.8% (an improvement of 7.9%) and mAP@0.5:0.95 reaching 62.0% (an improvement of 5.3%). This advancement significantly augments the night-time wildlife detection accuracy under rainy conditions, concurrently diminishing false detections in such challenging environments.

Research on Person Re-Identification through Local and Global Attention Mechanisms and Combination Poolings.

This research proposes constructing a network used for person re-identification called MGNACP (Multiple Granularity Network with Attention Mechanisms and Combination Poolings). Based on the MGN (Multiple Granularity Network) that combines global and local features and the characteristics of the MGN branch, the MGNA (Multiple Granularity Network with Attentions) is designed by adding a channel attention mechanism to each global and local branch of the MGN. The MGNA, with attention mechanisms, learns the most identifiable information about global and local features to improve the person re-identification accuracy. Based on the constructed MGNA, a single pooling used in each branch is replaced by combination pooling to form MGNACP. The combination pooling parameters are the proportions of max pooling and average pooling in combination pooling. Through experiments, suitable combination pooling parameters are found, the advantages of max pooling and average pooling are preserved and enhanced, and the disadvantages of both types of pooling are overcome, so that poolings can achieve optimal results in MGNACP and improve the person re-identification accuracy. In experiments on the Market-1501 dataset, MGNACP achieved competitive experimental results; the values of mAP and top-1 are 88.82% and 95.46%. The experimental results demonstrate that MGNACP is a competitive person re-identification network, and that the attention mechanisms and combination poolings can significantly improve the person re-identification accuracy.

Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items.

Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model - EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model's efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.

psHarmonize: Facilitating reproducible large-scale pre-statistical data harmonization and documentation in R.

Combining pertinent data from multiple studies can increase the robustness of epidemiological investigations. Effective "pre-statistical" data harmonization is paramount to the streamlined conduct of collective, multi-study analysis. Harmonizing data and documenting decisions about the transformations of variables to a common set of categorical values and measurement scales are time consuming and can be error prone, particularly for numerous studies with large quantities of variables. The psHarmonize R package facilitates harmonization by combining multiple datasets, applying data transformation functions, and creating long and wide harmonized datasets. The user provides transformation instructions in a "harmonization sheet" that includes dataset names, variable names, and coding instructions and centrally tracks all decisions. The package performs harmonization, generates error logs as necessary, and creates summary reports of harmonized data. psHarmonize is poised to serve as a central feature of data preparation for the joint analysis of multiple studies.

Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders.

This study introduces a novel deep learning framework for lung health evaluation using exhaled gas. The framework synergistically integrates pyramid pooling and a dual-encoder network, leveraging SHapley Additive exPlanations (SHAP) derived feature importance to enhance its predictive capability. The framework is specifically designed to effectively distinguish between smokers, individuals with chronic obstructive pulmonary disease (COPD), and control subjects. The pyramid pooling structure aggregates multilevel global information by pooling features at four scales. SHAP assesses feature importance from the eight sensors. Two encoder architectures handle different feature sets based on their importance, optimizing performance. Besides, the model's robustness is enhanced using the sliding window technique and white noise augmentation on the original data. In 5-fold cross-validation, the model achieved an average accuracy of 96.40%, surpassing that of a single encoder pyramid pooling model by 10.77%. Further optimization of filters in the transformer convolutional layer and pooling size in the pyramid module increased the accuracy to 98.46%. This study offers an efficient tool for identifying the effects of smoking and COPD, as well as a novel approach to utilizing deep learning technology to address complex biomedical issues.

Assessment of the Effective Sensitivity of SARS-CoV-2 Sample Pooling Based on a Large-Scale Screening Experience: Retrospective Analysis.

BACKGROUND: The development of new large-scale saliva pooling detection strategies can significantly enhance testing capacity and frequency for asymptomatic individuals, which is crucial for containing SARS-CoV-2. OBJECTIVE: This study aims to implement and scale-up a SARS-CoV-2 screening method using pooled saliva samples to control the virus in critical areas and assess its effectiveness in detecting asymptomatic infections. METHODS: Between August 2020 and February 2022, our laboratory received a total of 928,357 samples. Participants collected at least 1 mL of saliva using a self-sampling kit and registered their samples via a smartphone app. All samples were directly processed using AutoMate 2550 for preanalytical steps and then transferred to Microlab STAR, managed with the HAMILTON Pooling software for pooling. The standard pool preset size was 20 samples but was adjusted to 5 when the prevalence exceeded 2% in any group. Real-time polymerase chain reaction (RT-PCR) was conducted using the Allplex SARS-CoV-2 Assay until July 2021, followed by the Allplex SARS-CoV-2 FluA/FluB/RSV assay for the remainder of the study period. RESULTS: Of the 928,357 samples received, 887,926 (95.64%) were fully processed into 56,126 pools. Of these pools, 4863 tested positive, detecting 5720 asymptomatic infections. This allowed for a comprehensive analysis of pooling's impact on RT-PCR sensitivity and false-negative rate (FNR), including data on positive samples per pool (PPP). We defined Ctref as the minimum cycle threshold (Ct) of each data set from a sample or pool and compared these Ctref results from pooled samples with those of the individual tests (ΔCtP). We then examined their deviation from the expected offset due to dilution [ΔΔCtP = ΔCtP - log2]. In this work, the ΔCtP and ΔΔCtP were 2.23 versus 3.33 and -0.89 versus 0.23, respectively, comparing global results with results for pools with 1 positive sample per pool. Therefore, depending on the number of genes used in the test and the size of the pool, we can evaluate the FNR and effective sensitivity (1 - FNR) of the test configuration. In our scenario, with a maximum of 20 samples per pool and 3 target genes, statistical observations indicated an effective sensitivity exceeding 99%. From an economic perspective, the focus is on pooling efficiency, measured by the effective number of persons that can be tested with 1 test, referred to as persons per test (PPT). In this study, the global PPT was 8.66, reflecting savings of over 20 million euros (US $22 million) based on our reagent prices. CONCLUSIONS: Our results demonstrate that, as expected, pooling reduces the sensitivity of RT-PCR. However, with the appropriate pool size and the use of multiple target genes, effective sensitivity can remain above 99%. Saliva pooling may be a valuable tool for screening and surveillance in asymptomatic individuals and can aid in controlling SARS-CoV-2 transmission. Further studies are needed to assess the effectiveness of these strategies for SARS-CoV-2 and their application to other microorganisms or biomarkers detected by PCR.

Ayurvedic Management of Presbycusis (Project TOPMAC): Protocol for an Exploratory Randomized Controlled Trial.

BACKGROUND: Presbycusis is characterized by sensorineural hearing loss in both ears at high frequencies, which affects more than half of the older adults by the age of 75 years and is often accompanied by tinnitus and cognitive deterioration. Unfortunately, there are no treatments available to restore hearing loss. Treatment mainly focuses on improving the quality of life and communication with hearing aids. Traditional medicine like Ayurveda also explains ailments of a similar nature as Badhirya and advises using drugs with antiaging and neuroprotective activity for treatment. In Ayurveda, Badhirya and Karnanada (senile deafness with tinnitus) are due to vitiation of Vata Dosha. Treatments such as topical oil pooling (Karnapurana) are usually advised to counter Vata, improve hearing capacity, and reduce tinnitus. Kshirabala Taila, a medicated oil formulation prepared with Sida cordifolia Linnaeus, is one of the most preferred oils for topical oil pooling in such conditions, as it has a definitive indication for sensory dysfunctions. Drugs like Withania somnifera (L.) Dunal (Ashwagandha) are also used, as they ameliorate neurodegeneration and help to improve cognitive dysfunction. OBJECTIVE: We propose an exploratory randomized controlled trial study for evaluating the efficacy of TOPMAC (Topical Oil Pooling with Kshirabala Taila and Supplementation of Ashwagandha Churna) in tinnitus suppression and hearing and cognitive function protection in patients aged 60-75 years with mild to moderate presbycusis. METHODS: A parallel, 2-group, exploratory randomized controlled trial will be conducted in an Indian Ayurvedic research center at its outpatient service. Participants (N=60) with mild to moderate presbycusis will be recruited by screening. Participants will be randomized (computer-generated 1:1) to receive either basic treatment and health education (BTHE) or BTHE+TOPMAC for 24 weeks. The primary objective is to compare the efficacy of TOPMAC with that of BTHE in the protection of hearing function. The secondary objective is to compare the efficacy of TOPMAC with that of BTHE in tinnitus suppression and cognitive function protection. RESULTS: This project was funded in January 2023. The institutional ethics committees at National Ayurveda Research Institute for Panchakarma (3/1/2020/NARIP/Tech/2036) and Institute for Communicative and Cognitive Neuro Sciences (IEC006) approved this study. The first patient was enrolled in September 2023; 22 participants were enrolled as of August 2024. The data analysis is yet to start, and the results are expected to be published by January 2025. CONCLUSIONS: If this exploratory trial is proven effective, it will steer the setting of a definitive randomized controlled trial to test whether the TOPMAC intervention can be incorporated as a cost-effective integrative approach for managing presbycusis. The Indian government has already launched a National Program for Prevention and Control of Deafness to benefit the deaf population. TOPMAC may later be considered for integration with the national program. TRIAL REGISTRATION: Clinical Trials Registry India CTRI/2023/04/051485; https://tinyurl.com/2h2hry3n. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55089.

Comparison of RT-PCR cycle threshold values between individual and pooled SARS-CoV-2 infected nasopharyngeal swab specimens.

The molecular reverse transcription-polymerase chain reaction (RT-PCR) testing of respiratory tract swabs has become mandatory to confirm the diagnosis of coronavirus disease 2019 (COVID-19). However, RT-PCR tests are expensive, require standardized equipment, and relatively long testing times, and the sample pooling method has been introduced to solve this issue. The aim of this study was to compare the cycle threshold (Ct) values of the individual sample and pooled sample methods to assess how accurate the pooling method was. Repeat RT-PCR examinations were initially performed to confirm the Ct values for each sample before running the pooled test procedure. Sample extraction and amplification were performed in both assays to detect ORF1ab, N, and E genes with a cut-off point value of Ct <38. Overall, there was no difference in Ct values between individual sample and pooled sample groups at all concentrations (p=0.259) and for all pooled sizes. Only pooled size of five could detect the Ct value in the pooled samples for all concentration samples, including low-concentration sample (Ct values 36 to 38). This study highlighted that pooled RT-PCR testing strategy did not reduce the quality of individually measured RT-PCR Ct values. A pool size of five could provide a practical technique to expand the screening capacity of RT-PCR.

SPCANet: congested crowd counting via strip pooling combined attention network.

Crowd counting aims to estimate the number and distribution of the population in crowded places, which is an important research direction in object counting. It is widely used in public place management, crowd behavior analysis, and other scenarios, showing its robust practicality. In recent years, crowd-counting technology has been developing rapidly. However, in highly crowded and noisy scenes, the counting effect of most models is still seriously affected by the distortion of view angle, dense occlusion, and inconsistent crowd distribution. Perspective distortion causes crowds to appear in different sizes and shapes in the image, and dense occlusion and inconsistent crowd distributions result in parts of the crowd not being captured completely. This ultimately results in the imperfect capture of spatial information in the model. To solve such problems, we propose a strip pooling combined attention (SPCANet) network model based on normed-deformable convolution (NDConv). We model long-distance dependencies more efficiently by introducing strip pooling. In contrast to traditional square kernel pooling, strip pooling uses long and narrow kernels (1×N or N×1) to deal with dense crowds, mutual occlusion, and overlap. Efficient channel attention (ECA), a mechanism for learning channel attention using a local cross-channel interaction strategy, is also introduced in SPCANet. This module generates channel attention through a fast 1D convolution to reduce model complexity while improving performance as much as possible. Four mainstream datasets, Shanghai Tech Part A, Shanghai Tech Part B, UCF-QNRF, and UCF CC 50, were utilized in extensive experiments, and mean absolute error (MAE) exceeds the baseline, which is 60.9, 7.3, 90.8, and 161.1, validating the effectiveness of SPCANet. Meanwhile, mean squared error (MSE) decreases by 5.7% on average over the four datasets, and the robustness is greatly improved.

Estimand Framework and Statistical Considerations for Integrated Analysis of Clinical Trial Safety Data.

BACKGROUND: Safety analyses play a pivotal role in drug development, ensuring the protection of patients while advancing innovative pharmaceuticals to market. A single study generally does not have sufficient sample size to evaluate all important safety events with reasonable precision and may not cover the full target population for the investigational treatment. Integrated analyses (pooled or meta-analysis) over several studies may be helpful in that regard. But without a structured conscious workflow accompanied with appropriate statistical methods for the integrated analysis, this can easily take a route compromising the interpretation. METHODS: In this article we apply the ICH estimand framework to clinical trial integration and summarize respective critical statistical assumptions to ensure the integrated analyses are interpretable. RESULTS: The estimand framework is valuable for developing principles for a deeper understanding of the critical statistical aspects of planning an integrated safety analysis. Our principles address the clinical question of interest, estimand and estimation. Special focus was given to the criteria for inclusion and exclusion of the component studies in the integrated analysis, and to integration of estimates pertaining to signal detection. CONCLUSION: Performing an integrated analysis and its preparatory steps calls for a good understanding of the clinical question of interest and its estimand, care and sound practice, to enable interpretation and avoid introducing unnecessary bias. It is valuable to use the estimand framework not only for efficacy evaluations, but also for safety evaluations in clinical trials and for integrated safety analyses.

PCB plug-in solder joint defect detection method based on coordinated attention-guided information fusion.

Printed Circuit Boards (PCBs) are the foundational component of electronic devices, and the detection of PCB defects is essential for ensuring the quality control of electronic products. Aiming at the problem that the existing PCB plug-in solder defect detection algorithms cannot meet the requirements of high precision, low false alarm rate, and high speed at the same time, this paper proposes a method based on spatial convolution pooling and information fusion. Firstly, on the basis of YOLOv3, an attention-guided pyramid structure is used to fuse context information, and multiple convolutions of different size are used to explore richer high-level semantic information; Secondly, a coordinated attention network structure is introduced to calibrate the fused pyramidal feature information, highlighting the important feature channels, and reducing the adverse impact of redundant parameters generated by feature fusion; Finally, the ASPP (Atrous Spatial Pyramid Pooling) structure is implemented in the original Darknet53 backbone feature extraction network to acquire multi-scale feature information of the detection targets. With these improvements, the average detection accuracy of the enhanced network has been elevated from 94.45 to 96.43%. This experiments shows that the improved network is more suitable for PCB plug-in solder defect detection applications.

Adaptive loss-guided multi-stage residual ASPP for lesion segmentation and disease detection in cucumber under complex backgrounds.

BACKGROUND: In complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction. RESULTS: An original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas. CONCLUSIONS: The LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions.

Semantic Segmentation Network Based on Adaptive Attention and Deep Fusion Utilizing a Multi-Scale Dilated Convolutional Pyramid.

Deep learning has recently made significant progress in semantic segmentation. However, the current methods face critical challenges. The segmentation process often lacks sufficient contextual information and attention mechanisms, low-level features lack semantic richness, and high-level features suffer from poor resolution. These limitations reduce the model's ability to accurately understand and process scene details, particularly in complex scenarios, leading to segmentation outputs that may have inaccuracies in boundary delineation, misclassification of regions, and poor handling of small or overlapping objects. To address these challenges, this paper proposes a Semantic Segmentation Network Based on Adaptive Attention and Deep Fusion with the Multi-Scale Dilated Convolutional Pyramid (SDAMNet). Specifically, the Dilated Convolutional Atrous Spatial Pyramid Pooling (DCASPP) module is developed to enhance contextual information in semantic segmentation. Additionally, a Semantic Channel Space Details Module (SCSDM) is devised to improve the extraction of significant features through multi-scale feature fusion and adaptive feature selection, enhancing the model's perceptual capability for key regions and optimizing semantic understanding and segmentation performance. Furthermore, a Semantic Features Fusion Module (SFFM) is constructed to address the semantic deficiency in low-level features and the low resolution in high-level features. The effectiveness of SDAMNet is demonstrated on two datasets, revealing significant improvements in Mean Intersection over Union (MIOU) by 2.89% and 2.13%, respectively, compared to the Deeplabv3+ network.

The issue of heterogeneity of MSC-based advanced therapy medicinal products-a review.

Mesenchymal stromal stem cells (MSCs) possess a remarkable potential for numerous clinical applications due to their unique properties including self-renewal, immunomodulation, paracrine actions and multilineage differentiation. However, the translation of MSC-based Advanced Therapy Medicinal Products (ATMPs) into the clinic has frequently met with inconsistent outcomes. One of the suspected reasons for this issue is the inherent and extensive variability that exists among such ATMPs, which makes the interpretation of their clinical efficacy difficult to assess, as well as to compare the results of various studies. This variability stems from numerous reasons including differences in tissue sources, donor attributes, variances in manufacturing protocols, as well as modes of administration. MSCs can be isolated from various tissues including bone marrow, umbilical cord, adipose tissue and others, each with its unique phenotypic and functional characteristics. While MSCs from different sources do share common features, they also exhibit distinct gene expression profiles and functional properites. Donor-specific factors such as age, sex, body mass index, and underlying health conditions can influence MSC phenotype, morphology, differentiation potential and function. Moreover, variations in preparation of MSC products introduces additional heterogeneity as a result of cell culture media composition, presence or absence of added growth factors, use of different serum supplements and culturing techniques. Once MSC products are formulated, storage protocols play a pivotal role in its efficacy. Factors that affect cell viability include cell concentration, delivery solution and importantly, post-thawing protocols where applicable. Ensuing, differences in administration protocols can critically affect the distribution and functionallity of administered cells. As MSC-based therapies continue to advance through numerous clinical trials, implication of strategies to reduce product heterogeneity is imperative. Central to addressing these challenges is the need for precise prediction of clinical responses, which require well-defined MSC populations and harmonized assessment of their specific functions. By addressing these issues by meaningful approaches, such as, e.g., MSC pooling, the field can overcome barriers to advance towards more consistent and effective MSC-based therapies.

Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries.

Background: In 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics. Methods: We obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage. Results: In each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries. Conclusions: Using AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings. Supplementary Information: The online version contains supplementary material available at 10.1186/s44263-024-00081-2.

Strategies for Increasing the Throughput of Genetic Screening: Lessons Learned from the COVID-19 Pandemic within a University Community.

Amidst the COVID-19 pandemic, the Polytechnic University of Setúbal (IPS) used its expertise in molecular genetics to establish a COVID-19 laboratory, addressing the demand for community-wide testing. Following standard protocols, the IPS COVID Lab received national accreditation in October 2020 and was registered in February 2021. With the emergence of new SARS-CoV-2 variants and safety concerns for students and staff, the lab was further challenged to develop rapid and sensitive diagnostic technologies. Methodologies such as sample-pooling extraction and multiplex protocols were developed to enhance testing efficiency without compromising accuracy. Through Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) analysis, the effectiveness of sample pooling was validated, proving to be a clear success in COVID-19 screening. Regarding multiplex analysis, the IPS COVID Lab developed an in-house protocol, achieving a sensitivity comparable to that of standard methods while reducing operational time and reagent consumption. This approach, requiring only two wells of a PCR plate (instead of three for samples), presents a more efficient alternative for future testing scenarios, increasing its throughput and testing capacity while upholding accuracy standards. The lessons learned during the SARS-CoV-2 pandemic provide added value for future pandemic situations.

Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data.

BACKGROUND: Pooling data from different sources will advance mental health research by providing larger sample sizes and allowing cross-study comparisons; however, the heterogeneity in how variables are measured across studies poses a challenge to this process. METHODS: This study explored the potential of using natural language processing (NLP) to harmonise different mental health questionnaires by matching individual questions based on their semantic content. Using the Sentence-BERT model, we calculated the semantic similarity (cosine index) between 741 pairs of questions from five questionnaires. Drawing on data from a representative UK sample of adults (N = 2,058), we calculated a Spearman rank correlation for each of the same pairs of items, and then estimated the correlation between the cosine values and Spearman coefficients. We also used network analysis to explore the model's ability to uncover structures within the data and metadata. RESULTS: We found a moderate overall correlation (r = .48, p < .001) between the two indices. In a holdout sample, the cosine scores predicted the real-world correlations with a small degree of error (MAE = 0.05, MedAE = 0.04, RMSE = 0.064) suggesting the utility of NLP in identifying similar items for cross-study data pooling. Our NLP model could detect more complex patterns in our data, however it required manual rules to decide which edges to include in the network. CONCLUSIONS: This research shows that it is possible to quantify the semantic similarity between pairs of questionnaire items from their meta-data, and these similarity indices correlate with how participants would answer the same two items. This highlights the potential of NLP to facilitate cross-study data pooling in mental health research. Nevertheless, researchers are cautioned to verify the psychometric equivalence of matched items.

Comparison of sociodemographic factors, healthcare utilisation by general practitioner visits, somatic hospital admissions, and medication use in Norway, Sweden, and Denmark.

PURPOSE: The healthcare systems in Scandinavia inform nationwide registers and the Scandinavian populations are increasingly combined in research. We aimed to compare Norway (NO), Sweden (SE), and Denmark (DK) regarding sociodemographic factors and healthcare. METHODS: In this cross-sectional study, we analyzed aggregated data from the nationwide Scandinavian registers. We calculated country-specific statistics on sociodemographic factors and healthcare use (general practitioner visits, admissions to somatic hospitals, and use of medicines). RESULTS: In 2018, population were 5295,619 (NO), 10,120,242 (SE), and 5781,190 (DK). The populations were comparable regarding sex, age, education, and income distribution. Overall, medication use was comparable, while there was more variation in hospital admissions and general practitioner visits. For example, per 1000 inhabitants, 703 (NO), 665 (SE), and 711 (DK) individuals redeemed a prescription, whereas there were 215 (NO), 134 (SE), and 228 (DK) somatic hospital admissions per 1000 inhabitants. General practitioner contacts per 1000 inhabitants were 7082 in DK and 5773 in NO (-data from SE). CONCLUSION: The Scandinavian countries are comparable regarding aggregate-level sociodemographic factors and medication use. Variations are noted in healthcare utilisation as measured by visits to general practitioners and admissions to hospitals. This variation should be considered when comparing data from the Scandinavian countries.

Spindle Detection Based on Elastic Time Window and Spatial Pyramid Pooling.

BACKGROUND: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations. METHODS: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales. RESULTS: Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance. CONCLUSIONS: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.