bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid generation advancement of RIL population and unlocking the potential of Rhizobium nodulation for improving crop yields in chickpea
Authors: SwathiRekha Nandigam , Mahendrakar Mahesh Damodhar , Rajasekhar Srungarapu , Chand Uttam , Gopalakrishnan Subramaniam , Thati Srinivas , Vatluri Srinivasa Rao , Srini Vadlamudi , Vemula Anilkumar , Kudapa Himabindu , Sriniva Samineni
Published: July 2024

Chickpeas, a widely cultivated legume, actively fix atmospheric nitrogen in root nodules through a symbiotic relationship with rhizobia bacteria. A recombinant inbred line (RIL) population, progressing from F2 to F7 generations, was developed in a short-period of 18 months using the Rapid Generation Advancement (RGA) protocol. The F7 RILs were evaluated during the 2020-21 and 2021-22 crop seasons under typical field conditions to quantify the effects of nodulation on seed yield (SY) and its associated traits. The analysis of variance revealed a highly significant difference (P < 0.01) among genotypes for seed yield and other agronomic traits, with no significant seasonal effect. In the pooled analysis, nodulating genotypes (NG) exhibited a substantial increase (P < 0.01) in SY (62.55%), 100-seed weight (SW100; 12.21%), harvest index (HI; 6.40%), number of pods per plant (NPPP; 39.55%), and number of seeds per plant (NSPP; 44.37%) compared to non-nodulating genotypes (NNG). Both NG and NNG exhibited a significant (P < 0.01) positive correlation between SY and NPPP (r=0.64 and 0.63), NSPP (r=0.66 and 0.61), HI (r=0.27), and number of primary branches per plant (PBr) (r=0.31), respectively. The top-performing genotypes for yield and related traits were predominantly nodulating. Genotype-trait bi-plot analysis identified nine nodulating genotypes as the most adaptable across the two seasons—six for SY, plant height, SW100, and three for days to first flowering and maturity. These findings underscore the critical role of nodulation in maximizing chickpea yields and the significant yield penalties associated with non-nodulation. To boost chickpea production, future breeding efforts should focus on developing genotypes with high compatibility with rhizobium strains.
Treg Derived Amphiregulin Protects from Murine Lupus Nephritis via Tissue Reparative Effects
Authors: Ehnold Laura-Isabell , Melderis Simon , Hagenstein Julia , Warkotsch Matthias T. , Laas Viona , Feindt Frederic C. , Wu Hui , Huber Tobias B. , Grahammer Florian , Steinmetz Oliver M.
Published: July 2024

Systemic lupus erythematosus (SLE) is a common autoimmune disease that affects multiple organ systems. Among the most severe manifestations of SLE is lupus nephritis (LN), which causes particularly high morbidity. Recently, we identified amphiregulin (AREG), an epidermal growth factor receptor ligand, as a key mediator of LN via downregulation of CD4+ T-cell responses. In human LN, AREG is mainly produced by regulatory T cells (Tregs) and monocytes/macrophages (M/M). Since AREG´s functions have been shown to vary considerably depending on the source, we aimed to clarify the cell-type-specific roles of AREG using the pristane model of LN. Conditional knockout mice lacking Treg- but not M/M-derived AREG showed worse LN outcome at 12 and 15 months with increased glomerular cell proliferation, apoptosis and renal tissue fibrosis. Interestingly, immune responses were not relevantly affected by the lack of AREG from either leukocyte source, indicating a different mechanism. In this respect, in vitro studies demonstrated improved wound healing of murine mesangium and tubulus cells and enhanced regeneration and sprouting of human glomerular endothelial cells after incubation with recombinant AREG. These findings underscore the importance of Treg-derived AREG in tissue regeneration and protection from fibrosis in LN, highlighting AREG as a potential therapeutic target.
Swin Transformer with Part-Level Tokenization for Occluded Person Re-identification
Authors: Mishra Ranjit , Mondal Arijit , Mathew Jimson
Published: July 2024

To determine whether a pedestrian of interest has been captured by another distinct camera across a network of non-overlapping cameras, or by the same camera at a distinct time, is known as the problem of person re-identification and is considered one of the most fascinating challenges in computer vision. When query image of person of interest gets concealed, blocked, obscured or obstructed, the issue becomes considerably more challenging. Termed as occluded person re-identification, this covers scenarios that are closer to the real world crowded scenarios such as market place, airports, commercial malls, university campuses etc. Using a combination of global pedestrian level information along with part-level local feature information has increasingly has been shown to be a successful strategy for dealing with occluded person re-identification as it captures fine grained information from the non-occluded visible part.
This paper proposes a Swin Transformer with Part-Level Tokenization (SwinPLT) model that uses a Swin Transformer-based backbone enhanced with Singular Value Decomposition (SVD). The proposed model leverages the hierarchical representation learning capabilities of Swin Transformer, combined with SVD to extract uncorrelated local tokens. Our approach aims to enhance the model's discriminative ability by effectively handling occlusions in person images. Employing a combination of hard triplet loss and cross-entropy loss, the proposed SwinPLT surpasses the state-of-the-art results by at least 18.14% Rank1-accuracy and 17.28% mAP on the Occluded DukeMTMC-reID dataset. On the Occluded-ReID dataset, the proposed SwinPLT model outperforms the other alternative approaches by 9.06% Rank1-accuracy and 7.71% mAP. On P-DukeMTMC-reID dataset, our model shows an improvement of 1.7% Rank1-accuracy and 2.4% mAP, whereas on Partial-iLIDS, it shows an improvement of 11.8% Rank1-accuracy and 4.26% mAP. We will be making the code and the model publically available at https://github.com/Ranjitkm2007/SwinPLT
3D Human Mesh Recovery with Learned Gradient
Authors: Song Yuanyuan , Zhou Hui
Published: July 2024

3D human body reconstruction is a research hotspot in computer vision. In this article, we propose a method for 3D human mesh recovery with learned gradient. We use gradient descent networks to predict model parameter updates. Previous studies have focused on 3D pose estimation, often ignoring the importance of body shape reconstruction. In order to improve the accuracy of human body model shape estimation, we introduce silhouette information that can represent human body shape, using silhouette and 2D joints as inputs to the neural network. In addition, throughout the entire training process, we only need the SMPL parameterized human pose dataset, without any image to 3D correspondence. The network learns effective pose and shape subspaces from this data, and performs optimization more effectively in that subspace. It has been proven that our method can achieve advanced performance on public datasets. Even on challenging datasets in the wild, competitive results can be obtained.
Comparative Study of GPT-4.0, ERNIE Bot 4.0, and GPT-4o in the 2023 Chinese Medical Licensing Examination
Authors: Lian Luoyu
Published: July 2024

This study systematically evaluates the performance of three advanced large language models (LLMs)—GPT-4.0, ERNIE Bot 4.0, and GPT-4o—in the 2023 Chinese Medical Licensing Examination. Employing a dataset of 600 standardized questions, we analyzed the accuracy of each model in answering questions from three comprehensive sections: Basic Medical Comprehensive, Clinical Medical Comprehensive, and Humanities and Preventive Medicine Comprehensive. Our results demonstrate that both ERNIE Bot 4.0 and GPT-4o significantly outperformed GPT-4.0, achieving accuracies above the national pass mark. The study further examines the strengths and limitations of each model, providing insights into their applicability in medical education and potential areas for future improvement. These findings underscore the promise and challenges of deploying LLMs in multilingual and multimodal educational contexts, suggesting a pathway towards integrating AI into medical training and assessment practices.
Off-shell strings II: Black hole entropy
Authors: Ahmadain Amr , Wall Aron C.
Published: July 2024

In 1994, Susskind and Uglum argued that it is possible to derive the Bekenstein-Hawking entropy A/4G_NA/4GN from string theory. In this article we explain the conceptual underpinnings of this argument, while elucidating its relationship to induced gravity and ER=EPR. Following an off-shell calculation by Tseytlin, we explicitly derive the classical closed string effective action from sphere diagrams at leading order in \alpha^{\prime}α′. We then show how to use this to obtain black hole entropy from the RG flow of the NLSM on conical manifolds. (We also briefly discuss the more problematic “open string picture” of Susskind and Uglum, in which strings end on the horizon.) We then compare these off-shell results with the rival “orbifold replica trick” using the on-shell \mathbb{C}/Z_{N}ℂ/ZN background, which does not account for the leading order Bekenstein-Hawking entropy-unless perhaps tachyons are allowed to condense on the orbifold. Possible connections to the ER=EPR conjecture are explored. Finally, we discuss prospects for various extensions, including prospects for deriving holographic entanglement entropy in the bulk of AdS.
Spectral solutions for the Schrödinger equation with a regular singularity
Authors: Mohile Pushkar , Ahmed Ayaz , Vishnu T. R. , Pichai Ramadevi
Published: July 2024

We propose a modification in the Bethe-like ansatz to reproduce the hydrogen atom spectrum and the wave functions. Such a proposal provided a clue to attempt the exact quantization condition (EQC) for the quantum periods associated with potentials V(x)V(x) which are of the form (V(x) = |x| + a/|x| + b/|x|^2 )(V(x)=|x|+a/|x|+b/|x|2). We validate the EQC proposal by showing that our computed Voros spectrum in the limit a,b → 0a,b→0 is matching well with the true spectrum of the familiar |x||x| potential. Thus we have given a route to obtain the spectral solution for the one dimensional Schrödinger equation involving potentials with regular singularity at the origin.
Training, attitudes, segregation of duties and internal control of church finances: an empirical study in Indonesia
Authors: Ahmar Nurmala , Berliana Simbolon Merintan , Prastowo Darminto Dwi
Published: July 2024

Church organizations are non-profit organizations operating in the religious sector. Church financial management requires good and adequate internal control to protect and secure church assets from fraud, damage, and waste due to ineffective use of assets. Sacralization is a factor that gives rise to blind trust in financial managers and spiritual leaders in the church. This study aims to analyze the influence of trained accounting personnel, attitudes toward the importance of internal control, and separation of duties on the level of internal control of church finances. The sample used was 115 pastors at the HKBP Church in Jakarta, Indonesia. Data were obtained by distributing questionnaires to pastors as respondents. The analysis technique is carried out using multiple regression. The results of this study show that trained accounting personnel, attitudes about the importance of internal control implementation, and the existence of separation of duties in church organizations have a significant effect on the level of internal control of church finances. The results of the study strengthen support for the implementation of effective church financial internal control, which requires increasing the competency of accounting staff through accounting training, the attitude of organizational members to support the implementation of the church’s financial internal control system to make it more accountable, and the existence of a clear separation of duties within the organization to avoid fraud.
Impact of credit rating downgrades and tightness of accounting standards on earnings management in listed SMES
Authors: Zhee Lim Ying , Azriati Che Azmi Anna , Hock Ng Tuan
Published: July 2024

Due to its nature, funding remains the main problem for listed small and medium-sized enterprises (SMEs) globally. To overcome such a problem, there is a trend of using credit rating as the benchmark to appraise funding opportunities and applications in listed SMEs. As credit rating levels vary across time, subject to the performance of the listed SMEs, changes in the credit rating levels might trigger attention from listed SMEs, and actions might then be taken by the management to ensure that the credit rating is at the desired level. Since the literature in this strand of study is limited, this study aimed to examine the effect of credit rating downgrade and tightness of accounting standards on earnings management in listed SMEs. Employing a 2x3 between-subjects experiment manipulating credit rating downgrades (category or notch) and tightness of accounting standards (less tight, moderately tight, tight), it is evidenced that credit rating downgrades, especially notch downgrades, lead to more earnings management behaviors in the presence of a tight and less tight set of accounting standards. Different classifications of credit rating downgrades – notches and categories – will have different implications for earnings management based on the extent to which they are subject to external monitoring. As a practical matter, it is recommended that regulators exercise equal monitoring regardless of whether credit rating downgrades occur by category or notch.
Drought mitigation operation of water conservancy projects under severe droughts
Authors: Ding Wei , Bao Aimei , Lin Jie , Luo Chengxin , Cao Hui , Zhang Dongjie
Published: July 2024

Severe droughts typically last for extended periods and result in substantial water shortages, posing challenges for water conservancy projects. This study proposed a framework for coordinating drought mitigation operations across projects of various scales. First, the regulation and drought mitigation capacities of each project were analyzed, and thus critical reservoirs was identified. Subsequently, a joint regulation model for water supply, prioritizing projects based on their regulatory capacity from weak to strong, was established. An optimization model is then developed to determine the drought-limited levels for critical reservoirs, aiming to minimize water shortages. This model facilitates temporal coordination of water resources to prevent severe water shortages with frequent mild water shortages. Results in the Chuxionglucheng District of Chuxiong, Yunnan Province, during the severe drought period from 2009 to 2013 demonstrate significant reductions in water shortage. Specifically, the maximum shortage ratio decreased from 59 to 45% for agriculture and from 52 to 8% for industry. Moreover, emergency measures for drought mitigation were compared and recommended for regions with weak projects regulation. Overall, this framework offers a systematic approach to enhancing drought resilience across diverse water conservancy projects in severe drought conditions.
Water quality level estimation using IoT sensors and probabilistic machine learning model
Authors: T.R. Mahesh , Bhatia Khan Surbhi , Balajee A. , Almusharraf Ahlam , Gadekallu Thippa Reddy , Albalawi Eid , Kumar V. Vinoth
Published: July 2024

Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensembles the random forest model and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%.
A systematic literature review on adoption and impact of micro-irrigation
Authors: R. Selva Ganapathi , M. Shanthasheela
Published: July 2024

Water is an indispensable resource, and it is crucial for sustaining human life and agriculture. Nowadays, the share of water for agriculture is also shrinking. In the agricultural sector, micro-irrigation (MI) has emerged as a prominent technology for the efficient utilization of available water. However, understanding the adoption and impact of this technology is essential for its success. While existing studies on MI technologies were often limited to specific locations, this study addressed this gap by analysing 160 documents from the Scopus database through a systematic literature review and bibliometric analysis with thematic clustering. The study examined influential authors and nations, keyword co-occurrences, co-citations, and collaborations among authors and institutions. VOSviewer was utilized for bibliometric analysis. The research trend showed a steady increase in MI studies, with Zaccaria D. being the most productive author and the United States being the most influential country with several publications. Agricultural Water Management emerged as the most impactful journal, with Coelho E.F. being the most cited author. Additionally, three thematic clusters, namely effects of irrigation water, weed growth and crop yield, and irrigation and organic cultivation, were identified and discussed.
Towards the outbreak tail, what is the public opinion about wastewater surveillance in the United States?
Authors: Holm Rochelle H. , Anderson Lauren B. , Ness Heather D. , LaJoie A. Scott , Smith Ted
Published: July 2024

National opinions on a wide variety of public health topics can change over time and have highly contextual nuances. This study is a follow-up to prior inquiries into the knowledge of wastewater-based epidemiology, privacy concerns surrounding sample collection, and the use of data acquired, along with privacy awareness from an online survey conducted in the metropolitan United States during the winter of 2023. Mentions of wastewater-surveillance-related terms in the media remained common. Towards the outbreak tail in 2023, public support for surveillance of toxins (91%), diseases (91%), terrorist threats (87%), illicit drugs (70%), prescription medications (69%), and gun residue (60%) remained high. There was less support for surveillance of alcohol consumption (49%), mental illness (46%), healthy eating (37%), and lifestyle behaviors (35%). In terms of geographic scale, most respondents supported citywide surveillance (85%) with markedly lower levels of support for smaller (less anonymous) geographic scales covered by specific locations. Wastewater surveillance does not receive the public pushback that other COVID-19-related health system actors have witnessed. Instead, the public supports the expansion of wastewater surveillance as a standard to complement public health tools in other areas of health protection.
Optimum hydraulic retention time, the best microbial community genus, and carbon sources in woodchip denitrifying bioreactors: a mini review
Authors: Faramarzmanesh Sasan
Published: July 2024

Different factors affect the performance of denitrifying bioreactors. Hydraulic retention time (HRT), carbon source, and microbial community genus are three of the most important factors that affect the performance of denitrifying bioreactors. This study aimed to evaluate the best possible case of these factors. So, this study reviewed 42 papers between 2016 and 2023. Woodchips with 50% of particles >13 mm are recommended to be used in denitrifying bioreactors. Also, sedge, straw, and potato residues have better performance in comparison with woodchip. It can be seen that the performance of bioreactors is optimized at HRTs higher than 24 h. Microbial community genus belongs to Pseudomonas family bacteria, and Thauera and Halomonas species have an acceptable efficiency in unfavorable conditions.
Aqueous extracts of composted oil refinery sludge and their possible environmental impacts
Authors: Nath Snigdha , Das Subhasish
Published: July 2024

Oil refinery sludge (ORS) management is a global concern, yet information on its low-cost biotransformation possibility is fairly limited. We present a novel approach for ORS mitigation by Eisenia fetida and Eudrilus eugeniae and aerobic composting (AC) in producing clean aqueous extracts (compost teas). Detailed physicochemical characterization, phytotoxicity assays, economic feasibility comparisons, and mathematical equation-based environmental and human health hazard prediction studies were done for all the earthworm and non-earthworm processed compost teas. The E. eugeniae-compost tea reduced Cr, Pb, Cu, Cd, and Zn contents by 52.7, 61.2, 41.8, 80, and 93.8%, respectively. The earthworm-sourced teas showed lower ecological risk (<300) than their aerobic counterparts. Seed germination in Pisum sativum and Cicer arietinum was 2–2.5 folds higher with vermi-derived tea application. Economic assessments illustrated the superiority of E. eugeniae-based biotransformation with higher cash flow and a lower payback time of 1.53 years. The mathematical predictions on human health showed no alarming status for any of the vermi/aerobic compost teas. Overall results implied that vermicomposting is safer and more beneficial than AC in the ORS bioconversion. However, this study warrants further research in exploring the efficiency of other earthworm species, feedstock selection, or seasonal variability in ORS management.
Emerging Methods in the Identification of Bacterial Respiratory Tract Pathogens
Authors: Kiaghobadi Flora , Dehshahri Ali , Ghasemi Younes , Morowvat Mohammad Hossein
Published: July 2024

Aims:

Here, we will review different bacterial causes of respiratory
tract infections and discuss the available diagnostic methods. Moreover, we will
provide some recently published patents and newer techniques, such as respiratory
panels and omics approaches, and express the challenges in this path.

Background:

Respiratory tract infections (RTIs) include those infections that
can lead to the involvement of different respiratory parts, including the sinuses,
throat, airways, and lungs. Acute respiratory tract infection is the leading cause
of death from infectious illnesses worldwide. According to the World Health Organization,
1.6 to 2.2 million deaths have occurred due to acute respiratory infections
in children under five years of age. About 4 million people die annually
from respiratory infections, 98% of which are caused by lower respiratory infections.

Results:

Depending on the type of pathogen, the severity of the infection can
vary from mild to severe and even cause death. The most important pathogens
involved in respiratory tract infections include Streptococcus pneumoniae, Haemophilus
influenzae, and Moraxella catarrhalis. The symptoms are often similar,
but the treatment can vary greatly. Therefore, correct diagnosis is so important.
There are several methods for diagnosing respiratory infections. Traditional
tests include the culture of respiratory samples, considered the primary tool
for diagnosing respiratory infections in laboratories, and less common standard
tests include rapid and antigenic tests. It is essential to think that the culture
method is reliable. In the original method of diagnosing respiratory infections,
some bacteria were challenging to grow successfully, and many clinical laboratories
needed to be equipped for viral cultures. Another issue is the time to get the
results, which may take up to 7 days. Rapid and antigenic tests are faster but
need to be more accurate.

Conclusion:

The clinical laboratories are trying to be equipped with molecular
methods for detecting respiratory pathogens and identifying the genetic material
of the infectious agent in these new methods as the primary method in their agenda.
Multifaceted Disease Diagnosis: Leveraging Transfer Learning with Deep Convolutional Neural Networks on Chest X-Rays for COVID-19, Pneumonia, and Tuberculosis
Authors: Pal Vinay , Pabari Harshit , Indoria Shruti , Patel Shubham , Krishnan Deepa , Ravi Vinayakumar
Published: July 2024

Introduction

The three prevalent yet detrimental respiratory conditions, namely COVID-19, pneumonia, and tuberculosis, exhibit overlapping symptoms, making their differentiation challenging. However, their treatments are significantly divergent. Early detection emerges as a critical common factor for the effective management of these diseases. The pivotal initial step necessitates precise identification to initiate prompt prognosis. However, because of the lack of availability of experts in general and the inadequacy of the medical system on the whole, the problem of early detection is becoming highly concerning and, worst of all, time-consuming.

Objective

This research aimed to address this problem by examining and contrasting various deep Convolutional Neural Network (CNN) models that can accurately identify these illnesses, thereby assisting in their early detection.

Methods

4 pre-trained CNN architectures have been used in this work, namely EfficientNet-B0, VGG-16, InceptionNet, and ResNet-50, which have been implemented on the input dataset. Firstly, the data were collected and pre-processed, and then model training and testing were performed for all 4 pre-trained models specified above.

Results

After fine-tuning the models and evaluating the test metrics on the test dataset, the highest accuracy was observed for ResNet-50 and EfficientNet models, i.e., ~95%. Also, the precision and recall for both were very similar (approximately greater than 92%), indicating accurate and good-quality results.

Conclusion

In this work, a transfer learning system has been employed utilizing several pre-trained CNN architectures. Our findings have indicated that this system can effectively analyze X-ray images to diagnose COVID-19, pneumonia, and tuberculosis.
Association Study between rs10486567, rs13149290, rs1545985 and
rs6983267 with Incidence of Prostate Adenocarcinoma Among Iranians
Authors: Allahverdi Mina , Angaji Seyed Abdolhamid , Beikzadeh Behnaz , Roudi Raheleh
Published: July 2024

Introduction:

Prostate cancer is the second most frequent malignancy after lung cancer
among men, accounting for 7% of new cancers diagnosed around the world (15% in developed regions).
This disease has become more prevalent in Iran over the past few decades, moving up the
rankings from 13th in 1986 to 4th in 2005 and finally reaching third place in a recent study in 2016.
The purpose of this study is to investigate the association of rs10486567, rs13149290, rs6983267,
and rs1545985 with prostate cancer predisposition in the Iranian population. Due to the genetic heterogeneity,
each of the SNP should be studied separately in various communities.

Material and Methods:

This study was conducted as a case-control study on 200 patients referred
to Hashminejad Hospital in Tehran. 103 men with prostate adenocarcinoma were selected as case,
and 97 men with benign prostatic hyperplasia (BPH) were selected as control. In this research, according
to FAVORGENE-Taiwan extraction kit instructions, genomic DNA was extracted from
peripheral blood lymphocytesTetra-primer ARMS-PCR method was used for SNP genotyping.

Results:

The significance level at the first stage was p-value≤0.4. rs10486567 (p-value = 0.802) and
rs6983267 (p-value = 0.684). Based on the additive and multiplicative genetic model, no association
of these polymorphisms with prostate adenocarcinoma was observed. As a result, rs10486567
and rs6983267 were removed at this stage. rs13149290 (p-value=0.4) and rs1545985 (p-value=0.4)
at this stage were selected for the next stage. At the second stage rs13149290 (p-value=0.043)
showed a significant difference in the comparison between the two groups, but this difference was
not observed in relation to rs1545985 (p-value=0.392) rs1545985 was not associated with prostate
adenocarcinoma in the additive genetic model. According to P=0.013 and OR (95% CI) =3.837
(1.306-11.268), rs13149290 polymorphism is associated with prostate adenocarcinoma in the additive
genetic model (TT vs. CC).

Conclusion:

As a result of this study, it was observed that rs13149290 is associated with prostate
cancer in the Iranian population. Also this polymorphism is associated with Gleason score=7 and
PSA ≤4. Prostate cancer as an exposure effect is heterogeneous between different PSA levels. It is
suggested that this polymorphism be investigated in a larger population and in each ethnic group
separately.
Causes of Chromosome Breakage and Mis-segregation Affecting Pregnancy and Newborn Health: An Insight into Developing Reproductive Health Preventive Strategies
Authors: Mega Obukohwo Oyovwi , Ohwin Peggy Ejiro , Rume Rotu Arientare , Temitope Olowe Gideon , Oreoluwa Oyelere Abosede , Motunrayo Adelowo Joy
Published: July 2024

Chromosome abnormalities are a leading cause of pregnancy loss, developmental delays, and birth defects. These abnormalities arise from errors in chromosome structure (breakage) or number (missegregation) during cell division. Understanding the causes of these errors is crucial for developing effective preventive strategies to improve reproductive health. This paper aims to review the known causes of chromosome breakage and mis-segregation, emphasizing their impact on pregnancy and newborn health. It further explores potential preventive strategies for mitigating these risks. A comprehensive literature review was conducted using relevant databases, focusing on studies investigating the causes of chromosome abnormalities, their impact on pregnancy and newborn health, and potential preventive measures. Several factors contribute to chromosome breakage and mis-segregation, including Genetic Predisposition, Environmental Factors (environmental toxins, radiation), Maternal age, Lifestyle Factors (Smoking, alcohol consumption, and obesity), and Cellular Mechanisms. These abnormalities can manifest as various pregnancy complications, including Miscarriage, stillbirth, birth defects, and developmental Delays. The causes of chromosome breakage and mis-segregation are complex and multifactorial. Understanding these factors is crucial for developing effective preventive strategies. These strategies may include genetic counseling, pre-conception health optimization, environmental hazard mitigation, and advancements in assisted reproductive technologies. Further research is needed to identify specific interventions and personalize strategies based on individual risk factors. Addressing these causes and implementing preventive measures can significantly improve reproductive health outcomes and reduce the incidence of chromosome abnormalities affecting pregnancy and newborn health.
Smartphone-Delivered Attentional Bias Modification Training for Mental Health: Systematic Review and Meta-Analysis
Authors: Banire Bilikis , Orr Matt , Burns Hailey , McGowan Youna , Orji Rita , Meier Sandra
Published: July 2024

Background

Smartphone-delivered attentional bias modification training (ABMT) intervention has gained popularity as a remote solution for alleviating symptoms of mental health problems. However, the existing literature presents mixed results indicating both significant and insignificant effects of smartphone-delivered interventions.

Objective

This systematic review and meta-analysis aims to assess the impact of smartphone-delivered ABMT on attentional bias and symptoms of mental health problems. Specifically, we examined different design approaches and methods of administration, focusing on common mental health issues, such as anxiety and depression, and design elements, including gamification and stimulus types.

Methods

Our search spanned from 2014 to 2023 and encompassed 4 major databases: MEDLINE, PsycINFO, PubMed, and Scopus. Study selection, data extraction, and critical appraisal were performed independently by 3 authors using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. When necessary, we pooled the standardized mean difference with a 95% CI. In addition, we conducted sensitivity, subgroup, and meta-regression analyses to explore moderator variables of active and placebo ABMT interventions on reducing symptoms of mental health problems and attentional bias.

Results

Our review included 12 papers, involving a total of 24,503 participants, and we were able to conduct a meta-analysis on 20 different study samples from 11 papers. Active ABMT exhibited an effect size (Hedges g) of –0.18 (P=.03) in reducing symptoms of mental health problems, while the overall effect remained significant. Similarly, placebo ABMT showed an effect size of –0.38 (P=.008) in reducing symptoms of mental health problems. In addition, active ABMT (Hedges g –0.17; P=.004) had significant effects on reducing attentional bias, while placebo ABMT did not significantly alter attentional bias (Hedges g –0.04; P=.66).

Conclusions

Our understanding of smartphone-delivered ABMT’s potential highlights the value of both active and placebo interventions in mental health care. The insights from the moderator analysis also showed that tailoring smartphone-delivered ABMT interventions to specific threat stimuli and considering exposure duration are crucial for optimizing their efficacy. This research underscores the need for personalized approaches in ABMT to effectively reduce attentional bias and symptoms of mental health problems.

Trial Registration

PROSPERO CRD42023460749; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=460749
Examining the Impact of Simultaneous Alcohol and Cannabis Use on Alcohol Consumption and Consequences: Protocol for an Observational Ambulatory Assessment Study in Young Adults
Authors: Gunn Rachel L , Metrik Jane , Barnett Nancy P , Jackson Kristina M , Lipperman-Kreda Sharon , Miranda Jr Robert , Trull Timothy J , Fernandez Mary Ellen
Published: July 2024

Background

There is significant conflicting evidence as to how using cannabis while drinking alcohol (ie, simultaneous alcohol and cannabis use) impacts alcohol volume consumed, patterns of drinking, and alcohol-related consequences. The impact of simultaneous use on drinking outcomes may be influenced by several within-person (eg, contextual) and between-person (individual) factors.

Objective

This study was designed to examine naturalistic patterns of alcohol and cannabis use to understand how simultaneous use may impact drinking outcomes. The primary aims were to understand the following: (1) if simultaneous use is associated with increased alcohol consumption and riskier patterns of drinking, (2) if simultaneous use leads to increased alcohol consequences, and (3) how contextual circumstances moderate the impact of simultaneous use on consumption and consequences.

Methods

Data collection involves a 28-day ambulatory assessment protocol in which a sample of non–treatment-seeking young adults who report simultaneous use of alcohol and cannabis complete ecological momentary assessments (random, event-contingent, and time-contingent surveys) of alcohol and cannabis use, contexts, motives, and consequences on their personal smartphones while continuously wearing an alcohol biosensor bracelet. Participants also complete a baseline assessment, brief internet-based check-in on day 14, and a final session on day 28. Community-based recruitment strategies (eg, social media and flyers) were used to enroll 95 participants to obtain a target sample of 80, accounting for attrition.

Results

Recruitment and data collection began in May 2021 and continued through June 2024. Initial results for primary aims are expected in October 2024. As of March 2024, the project had recruited 118 eligible participants, of whom 94 (79.7%) completed the study, exceeding initial projections for the study time frame. Remaining recruitment will provide the capacity to probe cross-level interactions that were not initially statistically powered. Strengths of the project include rigorous data collection, good retention and compliance rates, faster-than-expected enrollment procedures, use of a novel alcohol biosensor, and successful adaptation of recruitment and data collection procedures during the COVID-19 pandemic.

Conclusions

This is the first investigation to assess the key momentary predictors and outcomes of simultaneous use as well as self-reported and objective (via alcohol biosensor) measures of alcohol consumption and patterns. The results of this study will inform prevention efforts and studies of individuals who use cannabis who are engaged in alcohol treatment.

International Registered Report Identifier (IRRID)

DERR1-10.2196/58685
Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review (Preprint)
Authors: AlSaad Rawan , Abd-alrazaq Alaa , Choucair Fadi , Ahmed Arfan , Aziz Sarah , Sheikh Javaid
Published: July 2024

BACKGROUND

In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation.

OBJECTIVE

The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF.

METHODS

A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis.

RESULTS

Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets.

CONCLUSIONS

These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.
Electronic Health Record–Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study (Preprint)
Authors: Shang Yong , Tian Yu , Lyu Kewei , Zhou Tianshu , Zhang Ping , Chen Jianghua , Li Jingsong
Published: July 2024

BACKGROUND

The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security.

OBJECTIVE

This study aims to propose an electronic health record (EHR)–oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy.

METHODS

The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data.

RESULTS

The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive.

CONCLUSIONS

The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.
Characterizing Public Sentiments and Drug Interactions during COVID-19: A Pretrained Language Model and Network Analysis of Social Media Discourse (Preprint)
Authors: Li Wanxin , Hua Yining , Zhou Peilin , Li Zhou , Xu Xin , Yang Jie
Published: July 2024

BACKGROUND

While COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects such as public opinions and suffer from reporting biases, inefficiency and long collection times.

OBJECTIVE

Harnessing drug-related data posted on social media in real time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study developed a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19 related drugs.

METHODS

This study constructed a full pipeline for COVID-19 related drug tweet analysis, utilizing pre-trained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of four core modules: named entity recognition (NER) and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names, target sentiment analysis (TSA) to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to COVID-19 and drug therapies between February 1, 2020, and April 30, 2022.

RESULTS

From a dataset comprising 2,124,757 relevant tweets sourced from 1,800,372 unique users, our NER model identified the top five most-discussed drugs: Ivermectin, Hydroxychloroquine, Remdesivir, Zinc, and Vitamin D. Sentiment and topic analysis revealed that public perception was predominantly shaped by celebrity endorsements, media hotspots, and governmental directives rather than empirical evidence of drug efficacy. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use.

CONCLUSIONS

This study evidences that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media-based public health analytics.