bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of an interrupted time series analysis (ITS) to evaluate the effect of universal dialysis policy from 2006 to 2016 in a province of northeastern Thailand
Authors: Pukdeesamai Piyalak , Sarakarn Pongdech , Anutrakulchai Sirirat
Published: April 2023

Background: An interrupted time series (ITS) analysis is a powerful tool for policy evaluation. In Thailand, chronic kidney disease (CKD) is a public health problem that requires a long recovery time and has a high treatment cost. The universal coverage policy for renal replacement therapy (universal dialysis policy), is used to treat this disease but policy evaluation using ITS analysis has rarely been conducted. This study applied ITS analysis to test the effect of such a policy between 2006 and 2016.

Methods: Data were retrieved from the electronic database of the health data center in Roi Et Province for the period between January 1, 2006 and December 31, 2016. 15,681 CKD stage 5 patients were included. The intervention under assessment was the universal health coverage system, which has been implemented since 2008.

Results: Results showed that before implementation of the universal dialysis policy, the overall trend of access to renal replacement therapy (RRT) slightly increased (0.74; 95% confidence interval (CI): 0.58, 0.90). After implementation of the policy, access sharply increased (6.10; 95%CI: 3.67, 8.54), while the linear trend after policy implementation also slightly increased (0.29; 95%CI: 0.05, 0.14). The stratified analysis showed the same linear directional trend before and immediately after implementing the universal dialysis policy.

Conclusions: Implementation of the universal dialysis policy positively impacted the rate of renal replacement therapy in CKD stage 5 patients, while access to health care services also increased.
A framework for doctoral education in developing students’ mental well-being by integrating the demand and resources of the program: An integrative review
Authors: Acharya Vrinda , Rajendran Ambigai , Shenoy Sandeep
Published: April 2023

Background: Research on doctoral students’ mental well-being has gained significant importance in recent years. The findings of such studies were uncertain about the critical demands and resources of a doctoral program that substantially influence the students’ mental health. This review aims to integrate the current evidence in bringing out the nature and significance of differentiated demands, contextual and personal resources, and their influence on the well-being of the students. 

Methods: An integrative literature review was conducted based on the five-stage framework of Whittemore and Knafl. The study identified 45 articles published from 2000 onwards following the Joanna Briggs Institute quality evaluation criteria and PRISMA reporting guidelines for selecting eligible articles. 

Results: The integrative review findings divulge that differentiated demands of doctoral programs were categorized into challenge-hindrance demands. The differentiated demands experienced by doctoral students were grouped as ambiguity in doctoral program structure, resource inadequacy, workload, complexity, and responsibility. Additionally, institutional support, research supervisory support, and intrinsic motivation were treated as essential resource in mitigating the effects of the differentiated demands of the doctoral program.

Conclusions: An integrated conceptual model was built exclusively for doctoral programs and suggests that the universities and supervisors design and structure healthy, constructive doctoral programs. As an outcome of the review, theoretical underpinnings of demands-resources and mental well-being are reported. The current review is an initial attempt to synthesize challenge-hindrance demands and contextual-personal resources in determining the mental well-being of doctoral students.
A systematic investigation of detectors for low signal-to-noise ratio EMG signals
Authors: Yuvaraj Monisha , Raja Priyanka , David Ann , Burdet Etienne , SKM Varadhan , Balasubramanian Sivakumar
Published: April 2023

Background: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.

Methods: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.

Results: The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.

Conclusions: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.
An automated sonic tomography system for the inspection of historical masonry walls
Authors: Ortega Javier , Meersman Marnix F.L. , Aparicio Sofía , Liébana Juan Carlos , Martín Rodrigo , Anaya José Javier , González Margarita
Published: April 2023

Background: The conservation of the built masonry heritage requires a comprehensive understanding of its geometrical, structural, and material characteristics. Non-destructive techniques are a preferred approach to survey historical buildings, given the cultural value of their fabric. However, currently available techniques are typically operated manually, consuming much time at operational and processing level and thus hindering their use for the on-site inspection of heritage structures.

Methods: A novel automated sonic tomography system was designed and built to inspect and obtain information about the inner structure and damage of historic masonry walls. The system consists of a hitting device mounted on a frame that can be placed adjacent to the wall under analysis. The hitting device can move along the surface within the frame area in X, Y and Z directions, generating the sonic wave. The receiving system is a scanning laser vibrometer, able to measure from the distance the displacement of a focused point over time, recording the wave when it reaches the opposite surface.

Results: Six stone masonry walls with different interior geometries were constructed at the laboratory by a professional stonemason. The construction of the walls was carefully documented, including the generation of detailed photogrammetric models of each single stone. The system was applied to survey the six masonry walls. Since the inner morphology of the walls is known, the resulting tomographic images could be compared with the ground truth.

Conclusions: Automating the inspection allowed to collect thousands of data in a few hours. New software was also developed to automate the processing of the data. Results are expected to highlight the potential of tomography to obtain quantitative information about the interior of heritage structures, while providing new tools that make the implementation of the technique more practical for professionals. Data, software and models have been made publicly available.
Colombia conectada El “Tíbet de Sudamérica” en perspectiva global, siglos XIX y XX
Authors: Buenaventura Gómez Laura Alejandra
Published: April 2023

Colombia conectada refuta la visión “aislacionista” de la historia de Colombia. En contra de la idea del “Tíbet de Sudamérica” (Alfonso López Michelsen), se evidencia que tanto en el siglo XIX como en el siglo xx hubo episodios de fuerte integración global. En ese sentido, los autores visibilizan las conexiones rotas por una historiografía enfocada en el Estado-nación como unidad de análisis. Sin embargo, como lo muestran los aportes en este texto, incluso la nación misma ha sido resultado de contactos y transferencias dentro de un contexto global. De ninguna manera, su formación puede explicarse de forma autorreferencial, como todavía ocurre en una buena parte de la producción académica del país. Periodos históricos tradicionalmente relacionados con el aislamiento político, la autarquía económica o el “desarrollo hacia adentro” se caracterizaron en realidad por múltiples formas de intercambio global, flujos, transferencias y conexiones, aunque el Estado no haya sido siempre protagónico en estos procesos. Así pues, los estudios de caso que conforman esta compilación demuestran el potencial de una historia global hecha en Colombia.
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
Authors: Li Gen , Cai Changxiao , Chen Yuxin , Wei Yuting , Chi Yuejie
Published: April 2023

This paper investigates a model-free algorithm of broad interest in reinforcement learning, namely, Q-learning. Whereas substantial progress had been made toward understanding the sample efficiency of Q-learning in recent years, it remained largely unclear whether Q-learning is sample-optimal and how to sharpen the sample complexity analysis of Q-learning. In this paper, we settle these questions: (1) When there is only a single action, we show that Q-learning (or, equivalently, TD learning) is provably minimax optimal. (2) When there are at least two actions, our theory unveils the strict suboptimality of Q-learning and rigorizes the negative impact of overestimation in Q-learning. Our theory accommodates both the synchronous case (i.e., the case in which independent samples are drawn) and the asynchronous case (i.e., the case in which one only has access to a single Markovian trajectory).
Learning through fun: board games cafes for undergraduate nursing students
Authors: Simpson Kate , Midgley Katherine
Published: April 2023

This article discusses the development, implementation and evaluation of clinical board games cafes in an undergraduate nurse education programme. Drawing on previous relevant literature about gaming approaches in education, the benefits and impact on student learning is presented. Thematic analysis of student feedback suggests that participation provided an opportunity to safely practise clinical scenarios and imbed concepts, as well as time to socialise to build support networks. Students also reported gaining confidence for their upcoming clinical placements. The benefits of a social opportunity timetabled during course teaching time was welcomed by students with commitments outside of the programme. The light-hearted cafe style environment can deliver a positive student experience and complement traditional teaching methods. Suggestions for future development include interdisciplinary sessions, development of games more specific to the mental health field and enabling students to get games out of the library for self-directed learning. Further evaluation of the impact of longer term learning through games is required.
An adaptable service: putting patients' needs first in nutrition nursing
Authors: Rubio Charlotte
Published: April 2023

Charlotte Rubio, Specialty Clinical Lead, Nutrition Support Team, Leicester Intestinal Failure Team, University Hospitals of Leicester NHS Trust ( charlotte.rubio@uhl-tr.nhs.uk ), winner of the BJN Nutrition Nurse of the Year 2023
Deep Neural Networks-based Classification Methodologies of Speech, Audio and Music, and its Integration for Audio Metadata Tagging
Authors: Park Hosung , Chung Yoonseo , Kim Ji-Hwan
Published: April 2023

Videos contain visual and auditory information. Visual information in a video can include images of people, objects, and the landscape, whereas auditory information includes voices, sound effects, background music, and the soundscape. The audio content can provide detailed information on the story by conducting a voice and atmosphere analysis of the sound effects and soundscape. Metadata tags represent the results of a media analysis as text. The tags can classify video content on social networking services, like YouTube. This paper presents the methodologies of speech, audio, and music processing. Also, we propose integrating these audio tagging methods and applying them in an audio metadata generation system for video storytelling. The proposed system automatically creates metadata tags based on speech, sound effects, and background music information from the audio input. The proposed system comprises five subsystems: (1) automatic speech recognition, which generates text from the linguistic sounds in the audio, (2) audio event classification for the type of sound effect, (3) audio scene classification for the type of place from the soundscape, (4) music detection for the background music, and (5) keyword extraction from the automatic speech recognition results. First, the audio signal is converted into a suitable form, which is subsequently combined from each subsystem to create metadata for the audio content. We evaluated the proposed system using video logs (vlogs) on YouTube. The proposed system exhibits a similar accuracy to handcrafted metadata for the audio content, and for a total of 104 YouTube vlogs, achieves an accuracy of 65.83%.
Comparison of Machine Learning Based on Category Theory
Authors: Zhao Heng , Chen Yixing , Fu Xianghua
Published: April 2023

In recent years, machine learning has been widely used in data analysis of network engineering. The increasing types of model and data enhance the complexity of machine learning. In this paper, we propose a mathematical structure based on category theory as a combination of machine learning that combines multiple theories of data mining. We aim to study machine learning from the perspective of classification theory. Category theory utilizes mathematical language to connect the various structures of machine learning. We implement the representation of machine learning with category theory. In the experimental section, slice categories and functors are introduced in detail to model the data preprocessing. We use functors to preprocess the benchmark dataset and evaluate the accuracy of nine machine learning models. A key contribution is the representation of slice categories. This study provides a structural perspective of machine learning and a general method for the combination of category theory and machine learning.
A Study about the Relationship between Vitamin D Level and Hip Fractures
Authors: Anthony Albert Naveen , Cherian Joe Joseph , Saji M. J
Published: April 2023

Introduction: Hip fractures are devastating injuries that most often affect the elderly and have a tremendous impact on both the healthcare system and society in general. The role of calcium and Vitamin D deficiency in bone metabolism is known and hence the necessity for further evaluation and studies to check its influence in hip fractures. Method: A descriptive type of study was conducted between October 2012 and July 2014 in St. John’s Medical College and Hospital. Patients who fulfilled the inclusion criteria were included in the study. Once the diagnosis of hip fractures was made then following tests were done on day 1 of admission – Vitamin D (CLIA method), calcium, phosphate, and alkaline phosphatase. Results: The study also showed that the incidence of the neck of femur fracture was comparable with intertrochanteric fractures. While neck of femur fractures was more common in female patients, while intertrochanteric fracture was common in male patients. These fractures were mainly seen in the age group between 61 and 70 years of age. The overall Vitamin D deficiency was 76% among all patients, with more predominance (84.6%) in female patients. Increased grades of fracture injury were seen in both fracture neck of femur and intertrochanteric fractures, when Vitamin D level was below 20 ng/ml Conclusion: The prevalence of Vitamin D deficiency among hip fractures necessitates correction of its serum value in the body. The treatment of Vitamin D deficiency may decrease the incidence of hip fractures and result in milder grades of fractures. This may help in better management of hip fractures and reduce the financial burden of healthcare costs. Keywords: Vitamin D, Vitamin D deficiency, Hip fractures
Journey Ahead!!
Authors: Shyam Ashok
Published: April 2023

Trauma International marches ahead with full determination to contribute in the field of trauma. We are thankful to the authors who are contributing to the journals work and we are sure this response will continue in the future too. We thank authors for their contribution in the May-December 2019 issue of Trauma International. This issue contains case reports on Occult Femoral Neck Fracture, Bilateral Uncemented Hemiarthroplasty, Bilateral femoral neck fracture in pregnancy and the review article on Galeazzi fracture associated with an ipsilateral fracture of ulna diaphysis and olecrane. We appreciate efforts of authors and hope for more contributions in the field of orthopaedic literature.
A Case Series of Occult Femoral Neck Fracture, Why Plain Radiograph is not Enough?
Authors: Butarbutar John , Aditya Rio , Suvarly Prettysia , Sindunata Nyoman Aditya
Published: April 2023

Background: About 10% of hip fractures are missed on initial presentation and potentially expose patients to increased morbidity and unnecessary complex surgeries. 2,3 CT-scan and MRI in the emergency settings have been proposed to avoid a misdiagnosis.3,6,7 Case Series: three cases (two female and one male patient) patients with hip pain were presented with normal x-ray on initial presentation. Further investigation using CT-scan and MRI revealed femoral neck fractures. Case 1 : A 77 years-old female presented with right hip pain after a ground-level fall. Plain radiograph was normal and the patient was discharged from emergency. One week later, her hip pain worsened and a follow up x-ray showed a displaced femoral neck fracture. The fracture then treated with bipolar hemiarthroplasty. Case 2 : An 87 years-old female presented with left hip pain after a fall in the bathroom. She was able to bear weight. Initial hip x-ray appeared normal. Further investigation with a pelvic CT scan revealed a displaced femoral neck fracture. Case 3 : A 49 years-old male presented with worsening right hip pain since more than a week. The patient was physically active and has no history of trauma or associated hip injury. Initial x-ray showed a normal hip but further imaging study using MRI revealed an undisplaced femoral neck fracture. The patient was treated conservatively as he refused to do surgery. Conclusion: Traumatic hip pain with negative plain radiograph should be followed by MRI or CT -scan to avoid misdiagnosis. Keywords : Occult hip fracture, Missed hip fracture, Femoral Neck Fracture, Elderly patient
Simultaneous thickness and phase index measurements with a motion-free actively tunable Twyman–Green interferometer
Authors: Reza Syed Azer , Imeri Arjent
Published: April 2023

In this paper, we present a scheme to simultaneously measure the thickness and refractive index of parallel plate samples, involving no bulk mechanical motion, by deploying an electronically tunable Twyman–Green interferometer configuration. The active electronic control with no bulk mechanical motion is realized via the introduction of a tunable focus lens within the classical motion-based Twyman–Green interferometer configuration. The resulting interferometer is repeatable and delivers accurate estimates of the thickness and refractive index of a sample under test. Elimination of bulk motion also promises a potential for miniaturization. We develop a theoretical model for estimating sample thickness and index values using this reconfigurable interferometer setup and present detailed experimental results that demonstrate the working principle of the proposed interferometer.
Automated colonoscopy withdrawal phase duration estimation using cecum detection and surgical tasks classification
Authors: De Carvalho Thomas , Kader Rawen , Brandao Patrick , González-Bueno Puyal Juana , Lovat Laurence B. , Mountney Peter , Stoyanov Danail
Published: April 2023

Colorectal cancer is the third most common type of cancer with almost two million new cases worldwide. They develop from neoplastic polyps, most commonly adenomas, which can be removed during colonoscopy to prevent colorectal cancer from occurring. Unfortunately, up to a quarter of polyps are missed during colonoscopies. Studies have shown that polyp detection during a procedure correlates with the time spent searching for polyps, called the withdrawal time. The different phases of the procedure (cleaning, therapeutic, and exploration phases) make it difficult to precisely measure the withdrawal time, which should only include the exploration phase. Separating this from the other phases requires manual time measurement during the procedure which is rarely performed. In this study, we propose a method to automatically detect the cecum, which is the start of the withdrawal phase, and to classify the different phases of the colonoscopy, which allows precise estimation of the final withdrawal time. This is achieved using a Resnet for both detection and classification trained with two public datasets and a private dataset composed of 96 full procedures. Out of 19 testing procedures, 18 have their withdrawal time correctly estimated, with a mean error of 5.52 seconds per minute per procedure.
Performance analysis of a spectropolarimeter employing a continuous phase variation
Authors: Vasilescu Bogdan , Piron Pierre , Loicq Jérôme
Published: April 2023

The light emitted or reflected by a medium can exhibit a certain degree of polarization. Most of the time, this feature brings valuable information about the environment. However, instruments able to accurately measure any type of polarization are hard to build and adapt to inauspicious environments, such as space. To overcome this problem, we presented recently a design for a compact and steady polarimeter, able to measure the entire Stokes vector in a single shot. The first simulations revealed a very high modulation efficiency of the instrumental matrix for this concept. However, the shape and the content of this matrix can change with the characteristics of the optical system, such as the pixel size, the wavelength or the number of pixels. To assess the quality of the instrumental matrices for different optical characteristics, we analyze here the propagation of errors, together with the impact of different types of noise. The results show that the instrumental matrices are converging towards an optimal shape. On this basis, the theoretical limits of sensitivity on the Stokes parameters are inferred.
Observation of robust edge mode and in-gap corner mode in Kagome surface-wave photonic crystals
Authors: Shen Ke , Deng Wei-Min , Mo Hao-Chang , Shi Fu-Long , Ma Fei , Chen Xiao-Dong , Dong Jian-Wen
Published: April 2023

Recent theory has demonstrated that Kagome photonic crystals (PCs) support first-order and second-order topological phenomena. Here, we extend the topological physics of the Kagome lattice to surface electromagnetic waves and experimentally show a Kagome surface-wave PC. Under the protection of first-order and second-order topologies, both robust edge modes and in-gap corner modes are observed. The robust transport of edge modes is demonstrated by high transmission through the waveguide with a sharp bend. The localized corner mode is found at the corner with one isolated rod when a triangle-shaped sample is constructed. Our work not only shows a platform to mimic the topological physics in classical wave systems, but also offers a potential application in designing high-performance photonic devices.
Compact and scalable polarimetric self-coherent receiver using a dielectric metasurface
Authors: Soma Go , Nomoto Yoshiro , Umezawa Toshimasa , Yoshida Yuki , Nakano Yoshiaki , Tanemura Takuo
Published: April 2023

Polarimetric self-coherent systems using direct-detection-based Stokes-vector receivers (SVRs) are a promising technology to meet both the cost and capacity requirements of short-reach optical interconnects. However, conventional SVRs require a number of optical components to detect the state of polarization at high speed, resulting in substantially more complicated receiver configurations compared with the current intensity-modulation–direct-detection counterparts. Here, we demonstrate a simple and compact polarimetric self-coherent receiver based on a thin dielectric metasurface and a photodetector array (PDA). With a single 1.05-µm-thick metasurface device fabricated on a compact silicon-on-quartz chip, we implement functionalities of all the necessary passive components, including a 1×3 splitter, three polarization beam splitters with different polarization bases, and six focusing lenses. Combined with a high-speed PDA, we demonstrate self-coherent transmission of 20-GBd 16-ary quadrature amplitude modulation and 50-GBd quadrature phase-shift keying signals over a 25-km single-mode fiber. Owing to the surface-normal configuration, it can easily be scaled to receive spatially multiplexed channels from a multicore fiber or a fiber bundle, enabling compact and low-cost receiver modules for future highly parallelized self-coherent systems.
iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria
Authors: Roux Simon , Camargo Antonio Pedro , Coutinho Felipe H. , Dabdoub Shareef M. , Dutilh Bas E. , Nayfach Stephen , Tritt Andrew
Published: April 2023

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.
A multispecies framework for modeling adaptive immunity and immunotherapy in cancer
Authors: Qi Timothy , Vincent Benjamin G. , Cao Yanguang
Published: April 2023

Predator-prey theory is commonly used to describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor “shows” the body) and the T-cell receptor (TCR) repertoire (how well the body “sees” cancer cells). The tumor immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poor response to immunotherapy in some tumor types, suggesting that the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Importantly, while tumor and T-cell populations are known to compete with each other for intratumoral resources, whether between-lineage competition among peripheral T cells influences the TCR repertoire is unknown and difficult to interrogate experimentally. Computational models may offer a way to investigate these phenomena and deepen our understanding of the tumor-immune axis. Here, we construct a predator-prey-like model and calibrate it to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of antigen-specific T-cell lineages and their consumption of both lineage-specific antigenic resources and lineage-agnostic, shared resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes; recapitulated response-associated effects of immunotherapy, including immunoediting; and allowed exploration of treatment of tumors with varying growth and mutation rates.
Mind the gap: Data availability, accessibility, transparency, and credibility during the COVID-19 pandemic, an international comparative appraisal
Authors: Rotulo Arianna , Kondilis Elias , Thwe Thaint , Gautam Sanju , Torcu Özgün , Vera-Montoya Maira , Marjan Sharika , Gazi Md. Ismail , Putri Alifa Syamantha , Hasan Rubyath Binte , Mone Fabia Hannan , Rodríguez-Castillo Kenya , Tabassum Arifa , Parcharidi Zoi , Sharma Beverly , Islam Fahmida , Amoo Babatunde , Lemke Lea , Gallo Valentina
Published: April 2023

Data transparency has played a key role in this pandemic. The aim of this paper is to map COVID-19 data availability and accessibility, and to rate their transparency and credibility in selected countries, by the source of information. This is used to identify knowledge gaps, and to analyse policy implications. The availability of a number of COVID-19 metrics (incidence, mortality, number of people tested, test positive rate, number of patients hospitalised, number of patients discharged, the proportion of population who received at least one vaccine, the proportion of population fully vaccinated) was ascertained from selected countries for the full population, and for few of stratification variables (age, sex, ethnicity, socio-economic status) and subgroups (residents in nursing homes, inmates, students, healthcare and social workers, and residents in refugee camps). Nine countries were included: Bangladesh, Indonesia, Iran, Nigeria, Turkey, Panama, Greece, the UK, and the Netherlands. All countries reported periodically most of COVID-19 metrics on the total population. Data were more frequently broken down by age, sex, and region than by ethnic group or socio-economic status. Data on COVID-19 is partially available for special groups. This exercise highlighted the importance of a transparent and detailed reporting of COVID-19 related variables. The more data is publicly available the more transparency, accountability, and democratisation of the research process is enabled, allowing a sound evidence-based analysis of the consequences of health policies.
Knowledge and attitude towards Ghana’s abortion law: A cross-sectional study among female undergraduate students
Authors: Abubakari Shamsiyatu , Gmayinaam Vincent Uwumboriyhie , Osei Eric
Published: April 2023

Ghana amended its abortion law to permit abortion under certain circumstances due to the impacts of unsafe abortion. Even though the abortion law in Ghana is liberal, most women do not utilize the services. Studies have shown that lack of knowledge and attitude towards abortion laws are the barriers deterring women from using safe abortion services. This study, therefore, assessed the knowledge and attitudes of future female health professionals towards Ghana’s abortion law. This was an institutional-based cross-sectional study among 240 female students undertaking undergraduate courses at the Fred Newton Binka School of Public Health (FNBSPH), the University of Health and Allied Sciences (UHAS), Ghana. Knowledge was measured with 9 items using yes or no responses while Attitude was measured using a five-point Likert scale with 14 items. Factors associated with poor knowledge among the students were determined using logistic regression. All analyses were done using STATA version 16.0. Of the 240 participants, 24 (10%) reported ever being pregnant. Among these pregnancies, 20 (83.3%) ended in abortions, with 15 (75%) of them unsafely done. The majority (53.3%) of the students knew the conditions under which abortion is allowed in Ghana and most (61.7%) of them had positive attitudes towards the abortion legislation in the country. The year of study (aOR: 0.06; 95%CI: 0.01–0.23), residential status (aOR: 0.44; 95%CI: 0.20–0.74) and poor attitude towards the abortion law (Aor:0.46; 95% CI: 0.26–0.82) were associated with poor abortion law knowledge among the students. This study has demonstrated that knowledge and attitude towards Ghana’s abortion legislation among the students was fairly good. Students’ year of study, residential status and attitude towards the abortion law were also found associated with poor knowledge of Ghana’s abortion law. Increasing young women’s knowledge of the abortion law may lead to more favourable attitudes towards abortion, improving the utilization of safe and legal abortion services.
Volatile organic compounds influence prey composition in Sarracenia carnivorous plants
Authors: Dupont Corentin , Buatois Bruno , Bessiere Jean-Marie , Villemant Claire , Hattermann Tom , Gomez Doris , Gaume Laurence
Published: April 2023

Sarracenia pitcher plants display interspecific differences in prey, so far only explained by pitcher morphology. We hypothesized that pitcher odours play a role in prey composition. We first compared odour and prey compositions among Sarracenia taxa grown together, forming a kinship gradient from S. purpurea known to capture primarily ants towards S. leucophylla known to capture many flying insects: S. purpurea, S. X mitchelliana, and S. X Juthatip soper & S. X leucophylla horticultural hybrids. We then measured several pitcher traits to disentangle the contributions of morphology and odour to prey variation. The pitcher odours were as diverse as those of generalist-pollinated flowers but with notable differences among taxa, reflecting their relatedness. VOC similarity analyses revealed taxon specificities, that mirrored those revealed by prey similarity analyses. S. X leucophylla stood out by being more specialised in flying insects like bees and moths and by releasing more monoterpenes known to attract flower visitors. S. X Juthatip soper trapped as many bees but fewer moths, sesquiterpenes contributing less to its scent. Ants and Diptera were the main prey of the other two with fatty-acid-derivative-dominated scents. Quantities of the different prey groups can be inferred 98% from quantities of the odour classes and pitcher dimensions. Two syndromes were revealed: ants associated with fatty-acid-derivatives and short pitchers; flying insects associated with monoterpenes, benzenoids and tall pitchers. In S. X leucophylla, emission rate of fatty-acid-derivatives and pitcher length explained most variation in ant captures; monoterpenes and pitcher length explained most variation in bee and moth captures; monoterpenes alone explained most variation in Diptera and wasp captures. Our results suggest that odours are key factors of the diet composition of pitcher plants. They support the hypothesis of perceptual exploitation of insect biases in carnivorous plants and provide new insights into the olfactory preferences of insect groups.
Diagnostic accuracy of Xpert MTB/RIF Ultra and culture assays to detect Mycobacterium Tuberculosis using OMNIgene-sputum processed stool among adult TB presumptive patients in Uganda
Authors: Sessolo Abdulwahab , Musisi Emmanuel , Kaswabuli Sylvia , Zawedde Josephine , Byanyima Patrick , Sabiiti Wilber , Walimbwa Stanley , Ola Joseph , Sanyu Ingvar , Lalitha Rejani , Kamya Moses , Davis Lucian , Worodria William , Huang Laurence
Published: April 2023

Background

Stool is a potential sample for diagnosing Mycobacterium tuberculosis (Mtb) in patients with difficulty in expectorating. However, high mycobacterial culture contamination rates and Xpert MTB/RIF Ultra test error rates on stool samples have limited its use. OMNIgene SPUTUM (OM-S) is a sample transport reagent with characteristics of sputum decontamination while maintaining viable Mtb. We evaluated the impact of OM-S on Mtb diagnostic yield from stool using smear microscopy, Xpert MTB/RIF Ultra, and culture among presumptive TB patients.

Methods

Paired stool and expectorated sputum samples were collected from consecutive Ugandan adults undergoing diagnostic evaluation for pulmonary TB between June 2018 and June 2019. Stool was divided into 2 portions: one was homogenized in OM-S (OM-S stool) and the other in PBS (PBS stool) as control. Both sputum and processed stool were tested for Mtb using concentrated smear fluorescence microscopy (CFM), Xpert MTB/RIF Ultra (Xpert) and Mycobacteria Growth Indicator Tube (MGIT) culture. Sensitivity, specificity, and predictive values for each test were calculated against sputum MGIT culture as the reference standard.

Results

Of the 200 participants, 120 (60%) were male, 73 (37%) were HIV positive (median CD4 120 cells/uL (IQR 43–297)) and 128 (64%) had confirmed pulmonary TB by sputum MGIT culture. Seven (4%) OM-S stool Xpert samples reported errors while 47 (25%) and 103 (61%) were contaminated on OM-S stool MGIT and PBS stool MGIT, respectively. OM-S stool MGIT was able to accurately diagnose 56 of the contaminated PBS stool MGIT samples compared to only 5 of the contaminated OM-S stool MGIT samples diagnosed by PBS stool MGIT.

Sensitivity (95% Confidence Interval, CI) 89% (83–94) for OM-S stool Xpert was higher compared to that of OM-S stool MGIT 60% (51–69) and PBS stool MGIT 42% (32–52). Specificity (95%CI) 91% (82–97) was also higher for OM-S stool Xpert compared to OM-S stool MGIT 64% (51–75) and PBS stool MGIT 26% (16–38).

Conclusion

Stool processed with OM-S showed potential to improve Mtb diagnostic yield and reduce rates of indeterminate results when tested on Xpert and MGIT culture. The method may thus be of value in Mtb detection among patients with difficulty to expectorate.
Conicity index as an indicator of abdominal obesity in individuals with chronic kidney disease on hemodialysis
Authors: Martins Cleodice Alves , do Prado Camila Bruneli , Santos Ferreira Júlia Rabelo , Cattafesta Monica , dos Santos Neto Edson Theodoro , Haraguchi Fabiano Kenji , Marques-Rocha José Luiz , Salaroli Luciane Bresciani
Published: April 2023

Background

The conicity index is indicated as a tool for assessing the nutritional status of renal individuals undergoing hemodialysis. Thus, this study aimed to estimate the prevalence of abdominal obesity using the conicity index in individuals with chronic kidney disease undergoing hemodialysis to verify its association with sociodemographic, clinical, and lifestyle factors.

Materials and methods

This is a cross-sectional study with 941 individuals undergoing hemodialysis in a metropolitan area in southeastern Brazil. The conicity index was estimated and cutoffs of 1.275 and 1.285 for men and women, respectively, were used. For the analysis of the results, binary logistic regression was performed and the odds ratio (OR) was estimated with their respective confidence intervals (95% CI).

Results

The conicity index was high in 56.54% of men (95% CI: 34.34–70.16) and 43.46% of women (95% CI: 38.45–55.20). We found that both adult men (OR = 3.71; 95% CI: 2.27–6.07) and adult women (OR = 4.06; 95% CI: 2.41–6.84) were more likely to have abdominal obesity, as well as self-declared mixed-raced (OR: 1.74; 95% CI: 1.01–3.00) and single men (OR: 1.64; 95% CI: 1.00–2.68).

Conclusions

The conicity index is an important anthropometric indicator to estimate abdominal obesity in individuals with chronic kidney disease on hemodialysis.
Body odor disgust sensitivity (BODS) is related to extreme odor valence perception
Authors: Zakrzewska Marta , Liuzza Marco Tullio , Olofsson Jonas K.
Published: April 2023

Odors are important disease cues, and disgust sensitivity to body odors reflects individual differences in disease avoidance. The body odor disgust sensitivity (BODS) scale provides a rapid and valid assessment of individual differences. Nevertheless, little is known about how individual differences in BODS might correlate with overall odor perception or how it is related to other differences in emotional reactivity (e.g., affect intensity). We investigated how BODS relates to perceptual ratings of pleasant and unpleasant odors. We aggregated data from 4 experiments (total N = 190) that were conducted in our laboratory, and where valence and intensity ratings were collected. Unpleasant odors were body-like (e.g., sweat-like valeric acid), which may provide disease cues. The pleasant odors were, in contrast, often found in soap and cleaning products (e.g., lilac, lemon). Across experiments, we show that individuals with higher BODS levels perceived smells as more highly valenced overall: unpleasant smells were rated as more unpleasant, and pleasant smells were rated as more pleasant. These results suggest that body odor disgust sensitivity is associated with a broader pattern of affect intensity which causes stronger emotional responses to both negative and positive odors. In contrast, BODS levels were not associated with odor intensity perception. Furthermore, disgust sensitivity to odors coming from external sources (e.g., someone else’s sweat) was the best predictor of odor valence ratings. The effects were modest in size. The results validate the BODS scale as it is explicitly associated with experimental ratings of odor valence.
Assessing spatiotemporal variability in SARS-CoV-2 infection risk for hospital workers using routinely-collected data
Authors: Wilson-Aggarwal Jared K. , Gotts Nick , Arnold Kellyn , Spyer Moira J. , Houlihan Catherine F. , Nastouli Eleni , Manley Ed
Published: April 2023

The COVID-19 pandemic has emphasised the need to rapidly assess infection risks for healthcare workers within the hospital environment. Using data from the first year of the pandemic, we investigated whether an individual’s COVID-19 test result was associated with behavioural markers derived from routinely collected hospital data two weeks prior to a test. The temporal and spatial context of behaviours were important, with the highest risks of infection during the first wave, for staff in contact with a greater number of patients and those with greater levels of activity on floors handling the majority of COVID-19 patients. Infection risks were higher for BAME staff and individuals working more shifts. Night shifts presented higher risks of infection between waves of COVID-19 patients. Our results demonstrate the epidemiological relevance of deriving markers of staff behaviour from electronic records, which extend beyond COVID-19 with applications for other communicable diseases and in supporting pandemic preparedness.
Normal high-sensitivity cardiac troponin for ruling-out inpatient mortality in acute COVID-19
Authors: Liu Alexander , Hammond Robert , Chan Kenneth , Chukwuenweniwe Chukwugozie , Johnson Rebecca , Khair Duaa , Duck Eleanor , Olubodun Oluwaseun , Barwick Kristian , Banya Winston , Stirrup James , Donnelly Peter D. , Kaski Juan Carlos , Coates Anthony R. M.
Published: April 2023

Introduction

Assessment of inpatient mortality risk in COVID-19 patients is important for guiding clinical decision-making. High sensitivity cardiac troponin T (hs-cTnT) is a biomarker of cardiac injury associated with a worse prognosis in COVID-19. We explored how hs-cTnT could potentially be used in clinical practice for ruling in and ruling out mortality in COVID-19.

Method

We tested the diagnostic value of hs-cTnT in laboratory-confirmed COVID-19 patients (≥18 years old) admitted to the Royal Berkshire Hospital (UK) between 1st March and 10th May 2020. A normal hs-cTnT was defined as a value within the 99th percentile of healthy individuals (≤14 ng/L), and an elevated hs-cTnT was defined as >14 ng/L. Adverse clinical outcome was defined as inpatient mortality related to COVID-19.

Results

A total of 191 COVID-19 patients (62% male; age 66±16 years) had hs-cTnT measured on admission. Of these patients, 124 (65%) had elevated hs-cTnT and 67 (35%) had normal hs-cTnT. On a group level, patients with elevated hs-cTnT had worse inpatient survival (p = 0.0014; Kaplan-Meier analysis) and higher risk of inpatient mortality (HR 5.84 [95% CI 1.29–26.4]; p = 0.02; Cox multivariate regression) compared to patients with normal hs-cTnT. On a per-patient level, a normal hs-cTnT had a negative predictive value of 94% (95% CI: 85–98%) for ruling out mortality, whilst an elevated hs-cTnT had a low positive predictive value of 38% (95% CI: 39–47%) for ruling in mortality.

Conclusions

In this study cohort of COVID-19 patients, the potential clinical utility of hs-cTnT appears to rest in ruling out inpatient mortality. This finding, if prospectively validated in a larger study, may allow hs-cTnT to become an important biomarker to facilitate admission-avoidance and early safe discharge.
Deep learning on graphs for multi-omics classification of COPD
Authors: Zhuang Yonghua , Xing Fuyong , Ghosh Debashis , Hobbs Brian D. , Hersh Craig P. , Banaei-Kashani Farnoush , Bowler Russell P. , Kechris Katerina
Published: April 2023

Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite recent advances, we remain limited in our ability to incorporate protein-protein interaction (PPI) network information with omics data for disease prediction. New deep learning methods including convolution Graph Neural Network (ConvGNN) has shown great potential for disease classification using transcriptomics data and known PPI networks from existing databases. In this study, we first reconstructed the COPD-associated PPI network through the AhGlasso (Augmented High-Dimensional Graphical Lasso Method) algorithm based on one independent transcriptomics dataset including COPD cases and controls. Then we extended the existing ConvGNN methods to successfully integrate COPD-associated PPI, proteomics, and transcriptomics data and developed a prediction model for COPD classification. This approach improves accuracy over several conventional classification methods and neural networks that do not incorporate network information. We also demonstrated that the updated COPD-associated network developed using AhGlasso further improves prediction accuracy. Although deep neural networks often achieve superior statistical power in classification compared to other methods, it can be very difficult to explain how the model, especially graph neural network(s), makes decisions on the given features and identifies the features that contribute the most to prediction generally and individually. To better explain how the spectral-based Graph Neural Network model(s) works, we applied one unified explainable machine learning method, SHapley Additive exPlanations (SHAP), and identified CXCL11, IL-2, CD48, KIR3DL2, TLR2, BMP10 and several other relevant COPD genes in subnetworks of the ConvGNN model for COPD prediction. Finally, Gene Ontology (GO) enrichment analysis identified glycosaminoglycan, heparin signaling, and carbohydrate derivative signaling pathways significantly enriched in the top important gene/proteins for COPD classifications.