bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data
Authors: Ter-Minassian Lucile , Viani Natalia , Wickersham Alice , Cross Lauren , Stewart Robert , Velupillai Sumithra , Downs Johnny
Published: December 2022

Objectives

Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood disorder, but often goes unrecognised and untreated. To improve access to services, accurate predictions of populations at high risk of ADHD are needed for effective resource allocation. Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.

Design

Retrospective population cohort study.

Setting

South London (2007–2013).

Participants

n=56 258 pupils with linked education and health data.

Primary outcome measures

Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. Ethnic group and language biases were weighted using a fair pre-processing algorithm.

Results

Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples (AUC 0.86 and 0.86, respectively) and clinical samples (AUC 0.72 and 0.70). Precision-recall curve analyses were less favourable. Sociodemographic biases were effectively reduced by a fair pre-processing algorithm without loss of accuracy.

Conclusions

ML approaches using linked routinely collected education and health data offer accurate, low-cost and scalable prediction models of ADHD. These approaches could help identify areas of need and inform resource allocation. Introducing ‘fairness weighting’ attenuates some sociodemographic biases which would otherwise underestimate ADHD risk within minority groups.
Escalating the limit of median survival time and predictors of mortality among preterm neonates in Northwest Ethiopia, 2021: a 1-year prospective follow-up study
Authors: Chanie Ermias Sisay , Engedaw Destaw , Eyayu Tahir , Admasu Fitalew Tadele , Amera Eninur Dejen , Azanaw Kefyalew Amogne , Kassaw Amare , Feleke Dejen Getaneh , Ayehu Gashaw Walle , Jimma Melkamu Senbeta , GebreEyesus Fisha Alebel , Moges Natnael , Woelile Tamiru Alene , Kerebeh Gashaw , Birrie Endalk
Published: December 2022

Objectives

To examine the survival rate and predictors of mortality among preterm neonates in the neonatal intensive care unit at South Gondar public hospitals, 2021.

Design

Prospective follow-up study.

Setting

South Gondar public hospitals, Northwest, Ethiopia.

Participants

We recruited 283 preterm neonates who were admitted at neonatal intensive care unit at selected hospitals from 15 February 2020 to 22 January 2021.

Outcome measures

The primary outcome measure of this study was the survival rate of preterm neonates in the neonatal intensive care unit. Moreover, the study assessed the predictors for the occurrence of mortality by the Cox-proportional hazard model. Data were entered into Epi data V.4.2 and exported to Stata V.14 statistical software for analysis. The log-rank test determines the survival difference between predictor variables.

Results

A total of 283 preterm neonates, 61 died during the follow-up. Born from antepartum haemorrhage mother (adjusted HR (AHR)=2.2 (95% CI 1.10 to 4.37)), being small weight for gestational age (AHR=4.6 (95% CI 2.22 to 9.53)), not having kangaroo mother care practice initiated (AHR=2.7 (95% CI 1.39 to 7.74)), hypothermia (AHR=4.0 (95% CI 1.96 to 8.30)) and perinatal asphyxia (AHR=3.9 (95% CI 1.97 to 7.94)) were significant predictors of preterm neonate mortality.

Conclusion

In this study, the preterm neonates survival rate (78.4%) and the median survival time (21 days) were found to be low. Preventing and managing the predictors, including an antepartum haemorrhagic mother, small weight for gestational age, hypothermia and prenatal asphyxia, is crucial. In addition, more emphasis should be placed on initiating universal kangaroo mother care practice soon after birth to increase the survival of preterm neonates.
Examining the barriers, facilitators and attitudes towards COVID-19 vaccine and public health measures for black communities in Canada: a qualitative study protocol
Authors: Ezezika Obidimma , Girmay Bethelehem , Adedugbe Toluwalope , Jonas Isaac , Thullah Yanaminah , Thompson Chris
Published: December 2022

Introduction

Black communities claim the highest number of cases and deaths due to COVID-19 in Canada. Generating culturally/contextually appropriate public health measures and strategies for vaccine uptake in black communities within Canada can better support the disproportionate impact of this pandemic. This study explores the barriers and enablers to public health measures limited to mask-wearing, disinfection, sanitation, social distancing and handwashing, as well as the barriers and attitudes towards COVID-19 vaccines among the black community.

Methods and analysis

We will use qualitative approaches informed by the widely accepted Consolidated Framework for Implementation Research (CFIR) to aid our investigation. We will conduct 120 semistructured interviews and five focus groups with black populations across the major provinces of Canada to understand the barriers and facilitators to public health measures, including barriers and attitudes towards COVID-19 vaccines. Data will be organised and analysed based on the CFIR. Facilitators and barriers to COVID-19 preventative measures and the barriers, facilitators and attitudes towards COVID-19 vaccines will be organised to explore relationships across the data.

Ethics and dissemination

This study was approved by the Social Sciences, Humanities and Education Research Ethics Board at the University of Toronto (41585). All participants are given information about the study and will sign a consent form in order to be included; participants are informed of their right to withdraw from the study. Research material will be accessible to all researchers involved in this study as no personal identifiable information will be collected during the key informant semistructured interviews and focus groups. The study results will be provided to participants and published in peer-reviewed journals.
Prevalence of self-reported lifetime medical diagnosis of depression in Brazil: analysis of data from the 2019 Brazilian National Health Survey
Authors: Mattiello Rita , Ospina Ayala Camila , Freitas Pedron Flaviana , Ferreira Isabel Cristina Schutz , Lessa Gaudie Ley Laura , Medeiros Paungartner Luciana , da Silva Martins Maiara , Bagatini Maria Amélia , Onofre Witt Batista Naycka , Oliveira Machado Cecagno Priscila , Kvitko de Moura Sara , López Tórrez Sergio , Munhoz Tiago N , Santos Iná S.
Published: December 2022

Objectives

To assess the prevalence of and factors associated with the lifetime medical diagnosis of depression in Brazil.

Design

Population-based, cross-sectional study.

Setting

Analysis of data from the 2019 Brazilian National Health Survey.

Participants

90 846 individuals aged ≥15 years were included.

Outcome measure

The self-reported medical diagnosis of depression at some point in one’s life was the main outcome. Prevalence ratios (PRs) with 95% CIs were calculated by Poisson regression with robust variance. The independent variables included the geographical area of residence, sociodemographic characteristics, current smoking status, alcohol abuse, daily screen time, and the presence of physical disorders and mental health comorbidities.

Results

The self-reported lifetime prevalence of medical diagnosis of depression was 9.9% (95% CI 9.5% to 10.2%). The probability of having received a medical diagnosis of depression was higher among urban residents (PR 1.23; 95% CI 1.12 to 1.35); females (2.75; 2.52 to 2.99); those aged 20–29 years (1.17; 0.91 to 1.51), 30–39 years (1.73; 1.36 to 2.19), 40–49 years (2.30; 1.81 to 2.91), 50–59 years (2.32; 1.84 to 2.93) and 60–69 years (2.27; 1.78 to 2.90) compared with those under 20 years; white-skinned people (0.69 (0.61 to 0.78) for black-skinned people and 0.74 (0.69 to 0.80) for indigenous, yellow and brown-skinned people compared with white-skinned people); those with fewer years of education (1.33(1.12 to 1.58) among those with 9–11 years, 1.14 (0.96 to 1.34) among those with 1–8 years and 1.29 (1.11 to 1.50) among those with 0 years compared with those with ≥12 years of education); those who were separated/divorced (1.43; 1.29 to 1.59), widowed (1.06; 0.95 to 1.19) and single (1.01; 0.93 to 1.10) compared with married people; smokers (1.26; 1.14 to 1.38); heavy screen users (1.31; 1.16 to 1.48) compared with those whose usage was <6 hours/day; those with a medical diagnosis of a physical disorder (1.80; 1.67 to 1.97); and individuals with a medical diagnosis of a mental health comorbidity (5.05; 4.68 to 5.46).

Conclusion

This nationwide population-based study of self-reported lifetime medical diagnosis of depression in Brazil showed that the prevalence was almost 10%. Considering the current Brazilian population, this prevalence corresponds to more than 2 million people who have been diagnosed with depression at some point in their lives.
Assessing medication use patterns in patients hospitalised with COVID-19: a retrospective study
Authors: Mueller Tanja , Kurdi Amanj , Hall Elliott , Bullard Ian , Wapshott Jo , Goodfellow Anna , Platt Niketa , Proud Euan , McTaggart Stuart , Bennie Marion , Sheikh Aziz
Published: December 2022

Objective

To describe patterns of medication use—that is, dexamethasone; remdesivir; and tocilizumab—in the management of patients hospitalised with COVID-19.

Design and setting

Retrospective observational study, using routinely collected, linked electronic data from clinical practice in Scotland. Data on drug exposure in secondary care has been obtained from the Hospital Electronic Prescribing and Medicines Administration System.

Participants

Patients being treated with the drugs of interest and hospitalised for COVID-19 between 1 March 2020 and 10 November 2021.

Outcomes

Identification of patients subject to the treatments of interest; summary of patients’ baseline characteristics; description of medication use patterns and treatment episodes. Analyses were descriptive in nature.

Results

Overall, 4063 patients matching the inclusion criteria were identified in Scotland, with a median (IQR) age of 64 years (52–76). Among all patients, 81.4% (n=3307) and 17.8% (n=725) were treated with one or two medicines, respectively; dexamethasone monotherapy accounted for the majority (n=3094, 76.2%) followed by dexamethasone in combination with tocilizumab (n=530, 13.0%). Treatment patterns were variable over time but roughly followed the waves of COVID-19 infections; however, the different drugs were used to varying degrees during the study period.

The median (IQR) treatment duration differed by medicine: dexamethasone 5 days (2–9); remdesivir 5 days (2–5); and tocilizumab 1 day (1–1). The overall median (IQR) length of hospital stay among all patients included in the study cohort was 9 days (5–17); 24.7% of patients died in hospital.

Conclusion

The use of adjuvant medicines in patients hospitalised with COVID-19 appears in line with evolving evidence and changing treatment guidelines. In-hospital electronic prescribing systems are a valuable source of information, providing detailed patient-level data on in-hospital drug use.
Efficacy of virtual reality assisted guided imagery (VRAGI) in a home setting for pain management in patients with advanced cancer: protocol for a randomised controlled trial
Authors: Hartshorn George , Browning Matthew , Chalil Madathil Kapil , Mau Fredric , Ranganathan Shyam , Todd Andrew , Bertrand Jeff , Maynard Allison , McAnirlin Olivia , Sindelar Kailan , Hernandez Rosalba , Henry Gomez Teny
Published: December 2022

Introduction

Patients with advanced cancer often experience high levels of debilitating pain and pain-related psychological distress. Although there is increasing evidence that non-pharmacological interventions are needed to manage their pain, pharmacologic modalities remain the preferred treatment . Guided imagery is a form of focused relaxation that helps create harmony between the mind and body and has been shown to significantly improve cancer pain. Our study presents Virtual Reality Assisted Guided Imagery (VRAGI) as a complementary treatment modality to manage chronic pain in patients with cancer. We will conduct a randomised controlled trial to test its impact on patients with advanced cancer in a home setting.

Methods and analysis

We will recruit 80 patients from Prisma Health, a tertiary-level healthcare centre based in Greenville, South Carolina, USA. The prospective 2×2 randomised controlled trial will randomise participants into four groups: (1) VRAGI, (2) laptop-assisted guided imagery, (3) VR (no guided imagery) and (4) laptop (no guided imagery). Patients allocated to VR groups will be trained to use a head-mounted display that immerses them in 3D audio–video content. The non-VR group will use a laptop displaying 2D video content. We will collect measures before and during the 3-week intervention as well as 3 weeks after the intervention ends. Measures will include patient-reported outcomes of pain, anxiety, depression and fatigue in addition to opioid use. The primary objective of the current study is to assess the efficacy of VRAGI on pain in the home setting. The secondary objective is to assess the efficacy of VRAGI on opioid use, anxiety, depression and fatigue.

Ethics and dissemination

This study was approved by the Prisma Health Institutional Review Board (#Pro00114598) in November 2021. All participants enrolled in the study will provide written informed consent. Dissemination will be through peer-reviewed publications and conference presentations.

Trial registration number

NCT05348174, clinicaltrials.gov.
Use of quality improvement methodology to improve care of women with hypertensive disease in pregnancy and haemorrhage in Yemen (low-income, high-insecurity setting)
Authors: Das Sabrina , Abdulwahid Emtiaz , Moisan Audrey , De Jesus Anna Kathrina
Published: December 2022

This safety improvement project used quality improvement methods in a Comprehensive Emergency Obstetric and Newborn Care facility in Yemen, managed by an international humanitarian non-governmental organisation. It is responsible for about 6500 deliveries per annum. Following a local review of maternal deaths and serious incidents in 2020–2021, care for women with severe hypertensive disease in pregnancy and postpartum haemorrhage were highlighted as potential areas for improvement. These are also the two most common reasons recorded for maternal mortality in Yemen (and in many low-income countries worldwide). We also wanted to foster an open and honest safety culture within the department that encouraged learning from error.

We used an inclusive approach in designing the improvements, with change ideas collated via frontline doctors, midwives and nurses working in the maternity unit. Data were collected via manual audit, and through routinely collected data. We focused on the following measures: number of incidents reported per month, documentation quality of antihypertensive prescriptions, fluid restriction practices in women with severe pre-eclampsia, number of minutes taken to control severe hypertensive episodes, postpartum haemorrhage identification rates and tranexamic acid stock usage. We evaluated the efficacy of team simulation training through precourse and postcourse questionnaires.

We found demonstrable improvement in our measures relating to treatment of women with hypertensive disorders of pregnancy, and in postpartum haemorrhage identification and treatment. Team simulation training was a difficult intervention to sustain but was received well with positive results during our test session. Incident reporting showed a temporary increase, but this effect was not sustained.

We concluded that quality improvement methodology is a valuable tool even in challenged healthcare settings such as this one, in an active conflict zone. Behaviour change in team culture and safety culture is harder to sustain and demonstrate without a long-term strategy.
Pre-arrest comorbidity burden and the future risk of out-of-hospital cardiac arrest in Korean adults
Authors: Hong Seok-In , Kim Youn-Jung , Kim Ye-Jee , Kim Won Young
Published: December 2022

Objective

To investigate the impact of pre-arrest comorbidities on future out-of-hospital cardiac arrest (OHCA) development using a nationwide dataset.

Methods

This population-based, matched case-control study used the national health insurance claims data relevant to OHCA in South Korea from January 2009 to December 2018. Case patients were randomly matched to controls by age, sex and date of cardiac arrest. Controls were defined as patients who did not experience OHCA based on claim codes in national health screening data. The comorbidity burden was assessed using the Charlson Comorbidity Index (CCI).

Results

A total of 191 370 OHCA patients were matched to 347 568 controls. The mean CCI in the case group was 3.76, which was significantly higher than that in the control group (1.75, p<0.001). Overall, OHCA was 1.35 (95% CI 1.34 to 1.35) times more likely to occur with every 1 point increase in the CCI. All other comorbidities constituting the CCI were associated with the OHCA risk (p<0.001). Patients with CCI ≥3 presented an OR of 3.71 (95% CI 3.67 to 3.76) for the risk of OHCA occurrence. This association was more pronounced in patients aged <70 years than in those aged ≥70 years (OR (95% CI) 16.07 (15.48 to 16.68) vs 6.50 (6.33 to 6.68)).

Conclusion

A high burden of pre-arrest comorbidity was associated with a higher risk of OHCA development, which was more pronounced in patients with less advanced age.
Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse
Authors: Liu Lunan , Ma Chao , Zhang Zhuoyu , Witkowski Matthew T , Aifantis Iannis , Ghassemi Saba , Chen Weiqiang
Published: December 2022

Background

Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited.

Methods

We established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19+) and CD19-negative (CD19−) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling.

Results

We revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19+relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19+antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19−relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction.

Conclusions

Our model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management.
An Improved TOPSIS Within the DEA Framework
Authors: Lu Jin-Cheng , Li Mei-Juan
Published: December 2022

Decision-maker (DM) can assign the weights of the distance measures in the relative closeness of technique for order preference by similarity to ideal solution (TOPSIS) for achieving his/her desirable ranking of alternatives. This phenomenon is called strategically setting distance measure weights in this study, which may affect the fairness of the results. In order to prevent this phenomenon, the idea of data envelopment analysis (DEA) is introduced to determine the objective weights of distance measures, and an improved TOPSIS is proposed in this study. The proposed method not only determines weights of the distance measures from an objective perspective and achieves the fully ranking of alternatives, but also provides the directions for improvement. Moreover, in comparison with the other methods, the advantages of the proposed method are analyzed, and the meanings and properties of the new relative closeness are discussed and proved. Finally, an example of evaluating the innovation development of high-tech industries in central and western regions of China is investigated to illustrate the effectiveness and the usefulness of the proposed method.
Eco-friendly methyl-ammonium tin-based planar p–n homojunction Perovskite solar cells: Design and performance estimation
Authors: Saha Prithick , Singh Sangeeta , Bhattacharya Sanjib
Published: December 2022

In this study, the design and performance estimation of tin (Sn)-based planar [Formula: see text]–[Formula: see text] homojunction perovskite solar cell (PSC) have been carried out. Here, the effect of maintaining the intrinsic active layer thickness and replacing the intrinsic active perovskite [Formula: see text]-layer of a standard [Formula: see text]-[Formula: see text]-[Formula: see text] structure of lead (Pb)-free PSCs with a [Formula: see text]–[Formula: see text] homojunction is fully investigated. When the active layer is divided into a [Formula: see text]–[Formula: see text] junction layer, it increases the photo-generated electrons and holes efficiency due to the built-in electric field of the junction. Furthermore, in order to get a better insight, the effects of various technological and device dimensional parameters on the performance of the reported PSC have been studied. It has been reported that the thickness of the p-side with methyl-ammonium tin triiodide (MASnI[Formula: see text] layer must be greater than the n-side of the MASnI3 layer. Meanwhile, its acceptors concentration should be slightly lower than the donors’ concentration on the n-side of MASnI3 layer to achieve maximum power conversion efficiency (PCE). Using the optimized parameters, our design demonstrates an open-circuit voltage ([Formula: see text] of 0.89 V, short-circuit current density ([Formula: see text] of 32.36 mA/cm2, fill factor (FF) of 74.40%, and PCE of 21.46%. This advocates the huge potential and great opportunities for deploying these Pb-free PSCs for eco-friendly photovoltaic applications.
CHANGE DETECTION OF BUILDINGS WITH THE UTILIZATION OF A DEEP BELIEF NETWORK AND HIGH-RESOLUTION REMOTE SENSING IMAGES
Authors: HUANG FENGHUA , SHEN GUIPING , HONG HUIQUN , WEI LIYING
Published: December 2022

To enhance the accuracy results of the change detection of buildings utilizing high-resolution remote sensing (HRRS) images, a novel method was proposed by combining both tensor and deep belief network (DBN). To optimize the description of the essential characteristics for changes in buildings, a tensor-based structure covering time-space-spectrum-shadow features integrated into the model (TSSS-Cube) is proposed. The changes occurring as a combination of shadow and spectral features and spatio-temporal autocorrelation at each pixel are represented by a third-order tensor to maintain the structural information and the constraint integrity between them. Then, a restricted Boltzmann machine (TC-RBM) that can be directly used to process TSSS-Cube data is designed, and the support tensor machine (STM) is used to replace the conventional backpropagation neural network at the top of the DBN to construct a multi-tensor deep belief network (MTR-DBN) composing of multi-layer TC-RBMs and an STM classifier. Finally, the multi-layer TC-RBMs in MTR-DBN are trained layer by layer, and the global parameters of the MTR-DBN are optimized by combining a limited number of labeled data and fine-tuning the STM classifier. The implementation of both supervised and unsupervised learning methods comprehensively provides advantages to increase the accuracy result of the MTR-DBN network to detect changes. Three representative different sub-regions are selected from the whole original experimental area respectively for building change detection experiments, and a dataset composed of double-temporal HRRS images in 2012 and 2016 is used as the related experimental dataset. The experimental results show that both a change detection accuracy result with a higher average and better detection efficiency is attained by the proposed method called the MTR-DBN when compared with other similar methods.
MEMORY EFFECT ANALYSIS USING PIECEWISE CUBIC B-SPLINE OF TIME FRACTIONAL DIFFUSION EQUATION
Authors: SHAFIQ MADIHA , ABDULLAH FARAH AINI , ABBAS MUHAMMAD , SM ALZAIDI AHMED , RIAZ MUHAMMAD BILAL
Published: December 2022

The purpose of this work is to study the memory effect analysis of Caputo–Fabrizio time fractional diffusion equation by means of cubic B-spline functions. The Caputo–Fabrizio interpretation of fractional derivative involves a non-singular kernel that permits to describe some class of material heterogeneities and the effect of memory more effectively. The proposed numerical technique relies on finite difference approach and cubic B-spline functions for discretization along temporal and spatial grids, respectively. To ensure that the error does not amplify during computational process, stability analysis is performed. The described algorithm is second-order convergent along time and space directions. The computational competence of the scheme is tested through some numerical examples. The results reveal that the current scheme is reasonably efficient and reliable to be used for solving the subject problem.
NOVEL BRIGHT AND KINK OPTICAL SOLITON SOLUTIONS OF FRACTIONAL LAKSHMANAN–PORSEZIAN–DANIEL EQUATION WITH KERR LAW NONLINEARITY IN CONFORMABLE SENSE
Authors: ALABEDALHADI M. , ALHAZMI S. , AL-OMARI S. , Al-SMADI M. , MOMANI S.
Published: December 2022

The fractional Lakshmanan–Porsezian–Daniel equation (LPD) is a significant complex model for the fractional Schrödinger family which arises in quantum physics. This paper explores new bright and kink soliton solutions of the space-time fractional LPD equation with the Kerr law of nonlinearity. By considering the conformable derivatives, the governing model is translated into integer-order differential equations with the aid of an appropriate complex traveling wave transformation. Dynamic behavior and phase portrait of traveling wave solutions are investigated. Further, various types of bright and kinked soliton solutions under definite parametric settings are discussed. Moreover, graphical representations of the obtained solution of the diverse fractional order are depicted to naturally illustrate the constructed solution.
An Enhanced Deep Neural Network-Based Approach for Speaker Recognition Using Triumvirate Euphemism Strategy
Authors: Pedalanka P. S. Subhashini , Ram Manchikalapudi Satya Sai , Rao Duggirala Sreenivasa
Published: December 2022

Automatic Speech Recognition (ASR) has been an intensive research area during the recent years in internet to enable natural human–machine communication. However, the existing Deep Neutral Network (DNN) techniques need more focus on feature extraction process and recognition accuracy. Thus, an enhanced deep neural network (DNN)-based approach for speaker recognition with a novel Triumvirate Euphemism Strategy (TES) is proposed. This overcomes poor feature extraction from Mel-Frequency Cepstral Coefficient (MFCC) map by extracting the features based on petite, hefty and artistry of the features. Then, the features are trained with Silhouette Martyrs Method (SMM) without any inter-class and intra-class separability problems and margins are affixed between classes with three new loss functions, namely A-Loss, AM-Loss and AAM-Loss. Additionally, the parallelization is done by a mini-batch-based BP algorithm in DNN. A novel Frenzied Heap Atrophy (FHA) with a multi-GPU model is introduced in addition with DNN to enhance the parallelized computing that accelerates the training procedures. Thus, the outcome of the proposed technique is highly efficient that provides feasible extraction features and gives incredibly precise results with 97.5% accuracy in the recognition of speakers. Moreover, various parameters were discussed to prove the efficiency of the system and also the proposed method outperformed the existing methods in all aspects.
Synthesis, microstructure and ionic conductivity of Li1+xAlxTi2−x(PO4)3-based composite ceramic electrolyte
Authors: Yu Xiaole , Cen Shangxu , Yu Zhongrui , Yang Peng , Ma Huirong , Chen Jingjing , Wang Dajian , Dong Chenlong , Mao Zhiyong
Published: December 2022

NASICON-type Li[Formula: see text]Al[Formula: see text]Ti[Formula: see text](PO4)3 (LATP) solid electrolyte features low cost of raw materials, high stability against O2/H2O, high ionic conductivity and high compatibility with cathode. Despite these merits, the direct contact of Li with LATP would trigger the reduction of Ti[Formula: see text] into Ti[Formula: see text], and thereupon decrease ion conductivity and enhance electron conductivity, thus limiting its application. In this work, the garnet Li[Formula: see text]La3Zr[Formula: see text]Ta[Formula: see text]O[Formula: see text] (LLZTO) particles were chosen to disperse in NASICON-type Li[Formula: see text]Al[Formula: see text]Ti[Formula: see text](PO4)3 matrix in order to strengthen stability against Li metal. During sintering, the matrix/particle reaction occurs, where LLZTO reacts with LATP, inducing the formation of LaPO4 particles. By optimization of LLZTO content, an enhanced ionic conductivity of 1.03 × 10[Formula: see text]S cm[Formula: see text] can be harvested. Moreover, introducing LLZTO particles into LATP matrix can slow down the degradation of the electrolyte to a certain extent and provide better interfacial stability of symmetrical battery than pure LATP solid electrolyte.
Utilizing publicly available datasets for identifying offshore salt strata and developing salt caverns for hydrogen storage
Authors: Allsop Craig , Yfantis Georgios , Passaris Evan , Edlmann Katriona
Published: December 2022

Hydrogen is expected to play a key role in a future climate-neutral economy by decarbonizing ‘hard to abate’ sectors, and importantly act as an energy carrier to balance intermittent renewable energy production, which, if stored, could be used to address grid-scale energy demands. It is evident that for unlocking the vast potential of hydrogen technologies, large-scale hydrogen storage with the capability of fast cyclic operations needs to be secured.

Underground salt caverns, where decades of operational experience exist, offer an attractive option for hydrogen storage due to the high hydrogen sealing potential of salt, capability for large injection/withdrawal flow rates and capacity for large volume storage.

This study presents a novel methodology for selecting offshore salt cavern sites based on publicly available datasets, which leads to eventually estimating regional hydrogen storage capacities. The Southern North Sea was investigated and a case study detailing a single diapiric salt structure is presented, demonstrating the strength and practicality of the framework as well as highlighting the potential of offshore salt cavern storage of hydrogen in the decarbonization energy journey of the UK.
Harmonizing Sensitive Data Exchange and Double-spending Prevention Through Blockchain and Digital Wallets: The Case of E-prescription Management
Authors: Schlatt Vincent , Sedlmeir Johannes , Traue Janina , Völter Fabiane
Published: December 2022

The digital transformation of the medical sector requires solutions that are convenient and efficient for all stakeholders while protecting patients’ sensitive data. One example that has already attracted design-oriented research are medical prescriptions. However, current implementations of electronic prescription management systems typically create centralized data silos, leaving user data vulnerable to cybersecurity incidents and impeding interoperability. Research has also proposed decentralized solutions based on blockchain technology, but privacy-related challenges have often been ignored. We conduct design science research to develop and implement a system for the exchange of electronic prescriptions that builds on two blockchains and a digital wallet app. Our solution combines the bilateral, verifiable, and privacy-focused exchange of information between doctors, patients, and pharmacies through verifiable credentials with a token-based, anonymized double-spending check. Our qualitative and quantitative evaluations as well as a security analysis suggest that this architecture can improve existing approaches to electronic prescription management by offering patients control over their data by design, a high level of security, sufficient performance and scalability, and interoperability with emerging digital identity management solutions for users, businesses, and institutions. We also derive principles on how to design decentralized, privacy-oriented information systems that require both the exchange of sensitive information and double-usage protection.
Composite g‐C3N4/NiCo2O4 with Excellent Electrochemical Impedance as an Electrode for Supercapacitors
Authors: Cui Danfeng , Fan Zheng , Fan Yanyun , Chen Hongmei , Li Penglu , Duan Xiaoya , Yan Shubin , Xu Hongyan , Xue Chenyang
Published: December 2022

For the development of supercapacitors, electrode materials with the advantages of simple synthesis and high specific capacitance are one of the very important factors. Herein, we synthesized g‐C3N4 and NiCo2O4 by thermal polymerization method and hydrothermal method, respectively, and finally synthesized NiCo2O4/g‐C3N4 nanomaterials by mixing, grinding, and calcining g‐C3N4 and NiCo2O4. NiCo2O4/g‐C3N4 nanomaterials are characterized by X‐ray diffraction and X‐ray photoelectron spectroscopy. The microscopic morphology, lattice structure, and element distribution of NiCo2O4/g‐C3N4 nanomaterials were characterized by scanning electron microscopy (SEM), transmission electron microscopy, high resoultion transmission electron microscopy, and mapping methods. The electrochemical performance and cycle stability of NiCo2O4/g‐C3N4 were tested in a 6 M KOH aqueous solution as electrolyte under a three‐electrode system. Due to the physical mixing structure of g‐C3N4 and NiCo2O4 nanomaterials, the electrochemical energy storage performance of NiCo2O4/g‐C3N4 supercapacitor electrodes is better than that of NiCo2O4 supercapacitor electrodes. At a current density of 1 A/g, the capacitances of NiCo2O4 and NiCo2O4/g‐C3N4 are 98.86 and 1,127.71 F/g, respectively. At a current density of 10 A/g, the capacitance of NiCo2O4/g‐C3N4 supercapacitor electrode maintains 70.5% after 3,000 cycles. NiCo2O4/g‐C3N4 electrode has excellent electrochemical performance, which may be due to the formation of physical mixing between NiCo2O4 and g‐C3N4, which has broad application prospects. This research is of great importance for the development of materials in high‐performance energy storage devices, catalysis, sensors, and other applications.
The Senescence‐Related Signature Predicts Prognosis and Characterization of Tumor Microenvironment Infiltration in Pancreatic Cancer
Authors: Hua Hao , Zheng Chenglong , Fan Jiling , Li Xiushen , Xie Wenfeng , Chen Jinxin , Yu Chao
Published: December 2022

Background. Senescence is thought to be an imperative effect on the development of cancer. However, few studies pay an attention to the senescence‐associated genes in pancreatic cancer (PC). The prognostic value of senescence‐related genes (SRGs) and their involvement in tumor microenvironment (TME) in the PC remain obscure. The aim of this research was to investigate the prognostic role of senescence‐associated genes and their affection in TME in PC. Methods. The transcriptome and clinical information of PC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Two SRG‐mediated molecular clusters were comprehensively identified. In total, data from the 285 PC patients were randomly used to develop a senescence‐associated gene signature in the training set and verified in the validation set. Immune microenvironment analysis pertained to senescence‐related genes was performed. Results. A SRG_score including five senescence‐associated genes was established to separate PC patients into two risk groups. High‐risk patients had worse overall survival than low‐risk patients. The result of the multivariate Cox regression analysis identified the risk score and stage as independent prognostic factors for PC patients. Receiver operating characteristic curve (ROC) analysis confirmed the credible predictive ability of the nomogram. The area under time‐dependent ROC curve (AUC) reached 0.746 at 1 year, 0.781 at 3 years, and 0.868 at 5 years in the training set and 0.653 at 1 year, 0.755 at 3 years, and 0.785 at 5 years in the validation set. Moreover, the SRG_score was associated with TME, tumor mutation burden (TMB), and chemotherapeutic drug sensitivity. Conclusions. This study found that the novel SRG_score could be an independent prognostic target for PC patients. Senescence‐associated genes had a vital impact on the immune microenvironment and the treatment of PC patients.
Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer’s Disease
Authors: Li Jialin , Gao Xinliang , Tang Mingbo , Wang Chi , Liu Wei , Tian Suyan
Published: December 2022

Lung cancer is the most common malignancy and is responsible for the largest cancer-related mortality worldwide. Alzheimer’s disease is a degenerative neurological disease that burdens healthcare worldwide. While the two diseases are distinct, several transcriptomic studies have demonstrated they are linked. However, no concordant conclusion on how they are associated has been drawn. Since these studies utilized conventional bioinformatics methods, such as the differentially expressed gene (DEG) analysis, it is naturally expected that the proportion of DEGs having either the same or inverse directions in lung cancer and Alzheimer’s disease is substantial. This raises the inconsistency. Therefore, a novel bioinformatics method capable of determining the direction of association is desirable. In this study, the moderated t-tests were first used to identify DEGs that are shared by the two diseases. For the shared DEGs, separate autoencoder (AE) networks were trained to extract a one-dimensional representation (pseudogene) for each disease. Based on these pseudogenes, the association direction between lung cancer and Alzheimer’s disease was inferred. AE networks based on 266 shared DEGs revealed a comorbidity relationship between Alzheimer’s disease and lung cancer. Specifically, Spearman’s correlation coefficient between the predicted values using the two AE networks for the Alzheimer’s disease test set was 0.825 and for the lung cancer test set was 0.316. Novel bioinformatics methods such as an AE network may help decipher how distinct diseases are associated by providing the refined representations of dysregulated genes.
McKittrick–Wheelock Syndrome: A Rare Case of Secretory Diarrhea
Authors: Rayad Mohammad Nabil , Mirza Noreen , Herrera-Gonzalez Maria Bernardeth , Bains Yatinder , Herrera-Gonzalez Sarahi
Published: December 2022

McKittrick–Wheelock syndrome commonly presents with a triad of chronic secretory diarrhea, electrolyte disturbances, and renal failure. Secretory diarrhea is due to active ion secretion secondary to secretagogue secretion (cyclic adenosine monophosphate and prostaglandin E2). The mainstay of treatment for these lesions is surgical since it will arrest the loss of electrolytes that may lead to serious clinical consequences. Nonsteroidal anti-inflammatory drugs (NSAIDs) such as indomethacin may be used to decrease electrolyte secretion in patients that desire a nonsurgical approach. Our patient is unique in that this is the first case of a tubular adenoma with high-grade dysplasia leading to MWS and progressing to circulatory collapse with severe electrolyte disturbances. Aggressive replacement of fluids and electrolytes is essential to the survival of these patients.
Qihuang Zhuyu Formula Attenuates Atherosclerosis via Targeting PPARγ to Regulate Cholesterol Efflux and Endothelial Cell Inflammation
Authors: Wang Mengxi , Xiang Qian , Sun Weixin , Zhang Haowen , Shi Ruijie , Guo Jun , Tong Huaqin , Fan Manlu , Ding Yuhan , Shi Haibo , Yu Peng , Shen Le , Wang Qiong , Chen Xiaohu
Published: December 2022

At present, due to the limitations of drug therapy targets for atherosclerosis, some patients fail to achieve satisfactory efficacy. Cholesterol efflux dysfunction and endothelial cell inflammation are considered to be important factors in the development of atherosclerosis. Peroxisome proliferator-activated receptor gamma (PPARγ), a promising therapeutic target for atherosclerosis, plays a dual role in regulating cholesterol efflux and endothelial cell inflammation. However, the use of PPARγ agonist in clinical practice is greatly limited as it could lead to water and sodium retention and hence result in congestive heart failure. Qihuang Zhuyu Formula (QHZYF) is a hospital preparation of Jiangsu Province Hospital of Chinese Medicine which has definite effect in the treatment of atherosclerosis, but its pharmacological mechanism has not been clear. In this study, we successfully predicted that QHZYF might regulate cholesterol efflux and endothelial inflammation via targeting PPARγ-mediated PPARγ/LXRα/ABCA1-ABCG1 and PPARγ/NF-κB p65 pathways by using UPLC-Q-TOF/MS, network pharmacology, bioinformatics analysis, and molecular docking technology. Subsequently, we confirmed in vivo that QHZYF could attenuate atherosclerosis in ApoE−/− mice and regulate the expression levels of related molecules in PPARγ/LXRα/ABCA1-ABCG1 and PPARγ/NF-κB p65 pathways of ApoE−/− mice and C57BL/6 wild-type mice. Finally, in in vitro experiments, we found that QHZYF could reduce lipid content and increase cholesterol efflux rate of RAW 264.7 cells, inhibit the inflammatory response of HUVECs, and regulate the expression levels of related molecules in the two pathways. In addition, the above effects of QHZYF were significantly weakened after PPARγ knockdown in the two kinds of cells. In conclusion, our study revealed that QHZYF attenuates atherosclerosis via targeting PPARγ-mediated PPARγ/LXRα/ABCA1-ABCG1 and PPARγ/NF-κB p65 pathways to regulate cholesterol efflux and endothelial cell inflammation. More importantly, our study offers a promising complementary and alternative therapy which is expected to make up for the limitation of current drug treatment methods and provide a valuable reference for new drugs development in the future.
Stem Cell Therapy for Diabetic Foot Ulcers: Theory and Practice
Authors: Nalisa David Lubasi , Moneruzzaman Md. , Changwe Geoffrey J. , Mobet Youchaou , Li Li Ping , Ma Yu Jin , Jiang Hong Wei
Published: December 2022

Diabetic foot ulcers are associated with increases in limb amputation, morbidity, and mortality. Recently, a stem cell application is emerging as promising adjuvant therapy. We presented available remedies by conducting a literature review on the application, safety, and efficacy of stem cell therapy. Relevant literature, including randomized control trials and article journals, was obtained from reputable search engines (PubMed, Scopus, and Web of Science). We analyzed five credible cohorts, with variable sources of stem cells, in a total of 216 participants, 151 males and 65 females, age (

mean
±
SD

) of

64.5
±
9.6

years. With an average success of 86.41% in all Wagner-II lesions, mesenchymal SCA (stem cell application) is safe and effective, hence can significantly prevent limb amputation.
Acute Thyroiditis in a Patient with Neck Trauma
Authors: Peralta Pearl Marian A. , Yu Jeryl Ritzi T. , Hernandez Edrome F. , dela Fuente Evangeline B.
Published: December 2022

The management of patients with major depressive disorder who present with self-injurious behavior is best optimized through a collaborative interprofessional approach. We describe a case of a 27-year-old woman without personal or family history of thyroid pathology who presented at the emergency department due to a suicide attempt by hanging. On examination, she was tachycardic with palpitations which persisted despite administration of analgesics and anxiolytics. Left temporal area swelling, left otorrhagia, and neck contusion were noted, involving consults with the Trauma, Neurosurgery, and Otorhinolaryngology teams. She was admitted to the psychiatric ward on account of persistent suicidal ideations. As part of the workup, thyroid function tests were done to rule out hypothyroidism as a cause of depressive symptoms. Results instead showed suppressed thyroid stimulating hormone and elevated free T4. Endocrinology service was consulted, and further workup showed absence of avid uptake of both thyroid glands on thyroid scan and undetectable thyrotropin receptor antibody level, supportive of a diagnosis of trauma-induced thyroiditis. This case increases awareness that trauma-induced thyroid dysfunction should be considered in patients with symptoms including, but not limited to, tachycardia and palpitations after a traumatic neck injury such as hanging.
Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy
Authors: Guo Tuo , Xu Fengjie , Ma Jinfang , Ge Fahuan
Published: December 2022

Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. In this study, the performance of one-dimensional convolutional neural network (1DCNN) machine learning algorithm was investigated for regression analysis of Antai pills spectral data. This algorithm was compared with other chemometric methods, including support vector machine regression (SVR) and partial least-square regression (PLSR) methods. The results showed that the 1DCNN model outperformed the PLSR and SVR models with similar data preprocessing for the three analytes (wogonoside, scutellarin, and ferulic acid) in Antai pills. Taking wogonoside as an example, the indices such as the correction coefficient of determination (

R
v
2

), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination (

R
p
2

), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. The evaluation metrics of 1DCNN are better than those of PLSR and SVR, and the prediction effect is the best, proving that 1DCNN has a good generalization ability. Especially with outliers of spectra, PLSR’s

R
p
2

decreased by 0.0181, SVR’s

R
v
2

decreased by 0.01, and 1DCNN’s

R
v
2

increased by 0.0009 and

R
p
2

decreased by 0.0057. The evaluation indices of 1DCNN have no significant change in comparison with no outliers and can still show good performance, which reflects the inclusiveness of the 1DCNN model for outliers. Simultaneously, the feasibility and robustness of the 1DCNN model in the application of near-infrared spectroscopy was verified, which has a certain application value.