bluuebunny/crossref_metadata_2025 huggingface dataset:
Unmasking the enigma of lipid metabolism in metabolic dysfunction-associated steatotic liver disease: from mechanism to the clinic
Authors: Rao Guocheng , Peng Xi , Li Xinqiong , An Kang , He He , Fu Xianghui , Li Shuangqing , An Zhenmei
Published: November 2023

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly defined as non-alcoholic fatty liver disease (NAFLD), is a disorder marked by the excessive deposition of lipids in the liver, giving rise to a spectrum of liver pathologies encompassing steatohepatitis, fibrosis/cirrhosis, and hepatocellular carcinoma. Despite the alarming increase in its prevalence, the US Food and Drug Administration has yet to approve effective pharmacological therapeutics for clinical use. MASLD is characterized by the accretion of lipids within the hepatic system, arising from a disarray in lipid provision (whether through the absorption of circulating lipids or de novo lipogenesis) and lipid elimination (via free fatty acid oxidation or the secretion of triglyceride-rich lipoproteins). This disarray leads to the accumulation of lipotoxic substances, cellular pressure, damage, and fibrosis. Indeed, the regulation of the lipid metabolism pathway is intricate and multifaceted, involving a myriad of factors, such as membrane transport proteins, metabolic enzymes, and transcription factors. Here, we will review the existing literature on the key process of lipid metabolism in MASLD to understand the latest progress in this molecular mechanism. Notably, de novo lipogenesis and the roles of its two main transcription factors and other key metabolic enzymes are highlighted. Furthermore, we will delve into the realm of drug research, examining the recent progress made in understanding lipid metabolism in MASLD. Additionally, we will outline prospective avenues for future drug research on MASLD based on our unique perspectives.
A dynamics association study of gut barrier and microbiota in hyperuricemia
Authors: Lv Qiulan , Zhou Jun , Wang Changyao , Yang Xiaomin , Han Yafei , Zhou Quan , Yao Ruyong , Sui Aihua
Published: November 2023

Introduction

The intricate interplay between gut microbiota and hyperuricemia remains a subject of growing interest. However, existing studies only provided snapshots of the gut microbiome at single time points, the temporal dynamics of gut microbiota alterations during hyperuricemia progression and the intricate interplay between the gut barrier and microbiota remain underexplored. Our investigation revealed compelling insights into the dynamic changes in both gut microbiota and intestinal barrier function throughout the course of hyperuricemia.

Methods

The hyperuricemia mice (HY) were given intragastric administration of adenine and potassium oxalate. Gut microbiota was analyzed by 16S rRNA sequencing at 3, 7, 14, and 21 days after the start of the modeling process. Intestinal permeability as well as LPS, TNF-α, and IL-1β levels were measured at 3, 7, 14, and 21 days.

Results

We discovered that shifts in microbial community composition occur prior to the onset of hyperuricemia, key bacterial Bacteroidaceae, Bacteroides, and Blautia exhibited reduced levels, potentially fueling microbial dysbiosis as the disease progresses. During the course of hyperuricemia, the dynamic fluctuations in both uric acid levels and intestinal barrier function was accompanied with the depletion of key beneficial bacteria, including Prevotellaceae, Muribaculum, Parabacteroides, Akkermansia, and Bacteroides, and coincided with an increase in pathogenic bacteria such as Oscillibacter and Ruminiclostridium. This microbial community shift likely contributed to elevated lipopolysaccharide (LPS) and pro-inflammatory cytokine levels, ultimately promoting metabolic inflammation. The decline of Burkholderiaceae and Parasutterella was inversely related to uric acid levels, Conversely, key families Ruminococcaceae, Family_XIII, genera Anaeroplasma exhibited positive correlations with uric acid levels. Akkermansiaceae and Bacteroidaceae demonstrating negative correlations, while LPS-containing microbiota such as Desulfovibrio and Enterorhabdus exhibited positive correlations with intestinal permeability.

Conclusion

In summary, this study offers a dynamic perspective on the complex interplay between gut microbiota, uric acid levels, and intestinal barrier function during hyperuricemia progression. Our study suggested that Ruminiclostridium, Bacteroides, Akkermansiaceae, Bilophila, Burkholderiaceae and Parasutterella were the key bacteria that play vital rols in the progress of hyperuricemia and compromised intestinal barrier, which provide a potential avenue for therapeutic interventions in hyperuricemia.
Prospective observational study of baloxavir marboxil in adults and adolescents with uncomplicated influenza from China
Authors: Liao Guichan , Xia Muye , Jiang Yuanhui , Chen Hongjie , Liao Wei , Peng Jie , Cai Shaohang
Published: November 2023

Introduction

There are limited data on the efficacy of baloxavir marboxil (baloxavir) versus oseltamivir in Chinese patients with influenza A.

Methods

This study is an observational real-world investigation encompassing 246 patients (baloxavir, n = 147; oseltamivir, n = 99) confirmed positive for influenza A. The choice between baloxavir and oseltamivir antiviral treatments was determined collaboratively by the clinician and the patient. A thorough comparative analysis was undertaken between the two groups, examining parameters such as the duration of fever and symptoms, viral load dynamics, lymphocyte changes, and enhancements in health-related quality of life (QoL).

Results

No significant differences were observed in demographic data between the two groups. The duration of fever was significantly shorter in the baloxavir group (P < 0.001). However, the duration of symptoms was not significant different (P = 0.167). Multivariable Cox analysis showed the independent factors affecting duration of fever were baloxavir treatment (HR = 2.033, P < 0.001), fever on day 1 (HR = 0.741, P = 0.010) and CRP level (HR = 1.009, P = 0.039). Moreover, sex (HR= 0.660, P = 0.019) and monocyte count (HR = 1.355, P = 0.018) were independent factors affecting the duration of symptoms. No significant difference in change of health-related quality of life (P > 0.05), positive rate of viral antigen on day 3 (P = 0.477) between the two groups. Remarkably, a mutation was observed in one case on the third-day after baloxavir treatment compared with first-day, from cysteine to serine at position 384 of the PA subunit.

Conclusion

In the clinical setting, baloxavir demonstrated comparable clinical benefits to oseltamivir, establishing its efficacy as an effective antiviral therapy for Chinese patients with influenza.
Characterization of a mobilizable megaplasmid carrying multiple resistance genes from a clinical isolate of Pseudomonas aeruginosa
Authors: Mei Li , Song Yang , Liu Dongxin , Li Yixiao , Liu Li , Yu Keyi , Jiang Mengnan , Wang Duochun , Wei Qiang
Published: November 2023

Introduction

The horizontal transfer of antibiotic resistance genes mediated by plasmids seriously hinders the effectiveness of modern medical treatment, and thus has attracted widespread attention. Additionally, the co-selection mechanism of antibiotic resistance genes (ARGs) and heavy metal resistance genes (MRGs) on mobile elements may further exacerbate the horizontal transfer of resistance genes.

Methods

In this study, a multidrug-resistant Pseudomonas aeruginosa strain, termed BJ86 (CHPC/NPRC1.4142), was isolated from a patient's sputum specimen. In vitro tests for antimicrobial susceptibility, conjugation, whole-genome sequencing, and bioinformatics analysis were used to explore the potential mechanisms of resistance and its spread.

Results and discussion

Sequencing analysis indicates that P. aeruginosa BJ86 carries an amazing 522.5 kb-length megaplasmid, pBJ86, which contained a 93.5 kb-length multiple resistance region (MRR); 18 kinds of genes were identified as ARGs in this region, including tmexCD-oprJ, blaDIM−1, qnrVC6 that mediate resistance to multiple antibiotics and the operons mer that mediates heavy metal mercury resistance. In addition, there is also an 80 kb variable region (VR) on the plasmid pBJ86, and the genes encoding relaxase and type IV coupling protein (T4CP) were determined in this region, both of which are related to the conjugation and transfer ability of the plasmid. Bioinformatics analysis shows that many functional genes have insertion sequences and transposases on their flanks, which may have accumulated in the plasmid pBJ86 after multiple acquisition events. Conjugated transfer and in vitro tests for antimicrobial susceptibility verified the mobility and plasmid pBJ86-mediated resistance. To our knowledge, we are the first to report a mobilizable megaplasmid that simultaneously carried tmexCD-oprJ, blaDIM−1, qnrVC6, and the operons mer and can be transferred with frequencies of 6.24 × 10−7 transconjugants per donor cell.
Quantitative iron–neuromelanin MRI associates with motor severity in Parkinson's disease and matches radiological disease classification
Authors: Hartono Septian , Chen Robert Chun , Welton Thomas , Tan An Sen , Lee Weiling , Teh Peik Yen , Chen Celeste , Hou Wenlu , Tham Wei Ping , Lim Ee Wei , Prakash Kumar M. , Shih Yao-Chia , Lee Kuan Jin , Tan Louis C. S. , Tan Eng King , Chan Ling Ling
Published: November 2023

Background

Neuromelanin- and iron-sensitive MRI studies in Parkinson's disease (PD) are limited by small sample sizes and lack detailed clinical correlation. In a large case–control PD cohort, we evaluated the diagnostic accuracy of quantitative iron–neuromelanin MRI parameters from the substantia nigra (SN), their radiological utility, and clinical association.

Methods

PD patients and age-matched controls were prospectively recruited for motor assessment and midbrain neuromelanin- and iron-sensitive [quantitative susceptibility mapping (QSM) and susceptibility map-weighted imaging (SMWI)] MRI. Quantitative neuromelanin–iron parameters from the SN were assessed for their discriminatory performance in PD classification using ROC analysis compared to those of qualitative visual classification by radiological readers of differential experience and used to predict motor severity.

Results

In total, 191 subjects (80 PD, mean age 65.0 years; 111 controls, 65.6) were included. SN masks showed (a) higher mean susceptibility (p < 0.0001) and smaller sizes after thresholding for low susceptibility (p < 0.0001) on QSM and (b) lower contrast range (p < 0.0001) and smaller sizes after thresholding for high-signal voxels (p < 0.0001) on neuromelanin-sensitive MRI in patients than in controls. Quantitative iron and neuromelanin parameters showed a moderate correlation with motor dysfunction (87.5%: 0.4< | r | <0.6, p < 0.0001), respectively. A composite quantitative neuromelanin–iron marker differentiated the groups with excellent performance (AUC 0.94), matching the diagnostic accuracy of the best-performing reader (accuracy 97%) using SMWI.

Conclusion

Quantitative neuromelanin–iron MRI is associated with PD motor severity and matched best-performing radiological PD classification using SMWI, with the potential to improve diagnostic confidence in the clinics and track disease progression and response to neuroprotective therapies.
Validation and comparison of the coding algorithms to identify people with migraine using Japanese claims data
Authors: Yamato Kentaro , Sano Hiromi , Hirata Koichi , Nakayama Takeo
Published: November 2023

Purpose

The study aimed to validate and compare coding algorithms for identifying people with migraine within the Japanese claims database.

Methods

This study used the administrative claim database provided by DeSC Healthcare, Inc., that was linked to the results of an online survey administered to adult users of the health app “kencom®.” The ability of the 12 algorithms to detect migraines using diagnostic records alone or with prescription records was evaluated based on sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). We used a migraine diagnosis judged based on respondents' self-reported symptoms according to the diagnostic criteria of the International Classification of Headache Disorders, version 3 (ICHD-3), as true.

Results

Of the 21,480 individuals, 691 had migraine according to the ICHD-3 criteria. The 12 algorithms had a sensitivity of 5.4–8.8%, specificity of 98.8–99.6%, PPVs of 19.2–32.5%, and NPVs of 96.9–97.0%. Algorithm 9 (migraine diagnostic records more than once AND at least one prescription record for migraine prophylaxis or triptans in the same month as diagnosis) produced the highest PPV, whereas Algorithm 2 (at least one diagnostic record of migraine or tension-type headache) had the highest sensitivity. Similar trends were observed when using the ID-Migraine or 4-item migraine screener, instead of the ICHD-3 criteria, for case ascertainment.

Conclusion

Strict algorithms, such as Algorithm 9, yielded a higher PPV but a lower sensitivity, and such algorithms may be suitable for studies estimating the relative risk. Conversely, algorithms based on a single diagnostic record, such as Algorithm 2, had a higher sensitivity and may be suitable for studies estimating the prevalence/incidence of disease. Our findings will help select a desirable algorithm for migraine studies using a Japanese claim database.
Embodied strategies for public speaking anxiety: evaluation of the Corp-Oral program
Authors: García-Monge Alfonso , Guijarro-Romero Santiago , Santamaría-Vázquez Eduardo , Martínez-Álvarez Lucio , Bores-Calle Nicolás
Published: November 2023

Background

Public speaking is an indispensable skill that can profoundly influence success in both professional and personal spheres. Regrettably, managing anxiety during a speech poses a significant challenge for many of the population. This research assessed the impacts of a Corp-Oral program, designed to manage public speaking anxiety in university students, based on, body awareness, embodied message techniques, simulation, embodied visualization, body transformation, and gesture enhancement.

Methods

Thirty-six students (61% women; Mage= 20.22, SD = 1.23 years) were randomly assigned to either an experimental group (n= 18), which underwent the Corp-Oral program, or a control group (n= 18). Self-perceived anxiety, heart rate, and electroencephalography were measured in a pre-test and a post-test.

Results

The study reveals that the Corp-Oral program significantly (p< 0.005) reduced both physiological responses (heart rate) and self-reported measures of anxiety. The alteration was more noticeable in self-reported anxiety measures (a decrease of 33.217%) than in heart rate (a decrease of 4.659%). During the speech, the experimental group exhibited increased cortical activation in areas related to emotional regulation, consciousness, sensorimotor integration, and movement control. A significant increase in frontal alpha asymmetry was observed for the experimental group in the post-test, but there were no significant variations in the theta/beta ratio.

Conclusion

These findings underline the benefit of managing public speaking anxiety not merely by reducing it but by channeling it through embodied strategies. These strategies could lead to greater action awareness that would cushion the physiological effect of the anxiety response and help generate a better self-perception of the anxiety state.
High-beta oscillations at EEG resting state and hyperconnectivity of pain circuitry in fibromyalgia: an exploratory cross-sectional study
Authors: Alves Rael Lopes , Zortea Maxciel , Serrano Paul Vicuña , Brugnera Tomedi Rafaela , Pereira de Almeida Rodrigo , Torres Iraci L. S. , Fregni Felipe , Caumo Wolnei
Published: November 2023

Background

Electroencephalography (EEG) has identified neural activity in specific brain regions as a potential indicator of the neural signature of chronic pain. This study compared the lagged coherence connectivity between regions of interest (ROIs) associated with the pain connectome in women with fibromyalgia (FM) and healthy women (HC).

Methods

We evaluated 64 participants (49 FM and 15 HC) during resting-state EEG sessions under both eyes open (EO) and eyes closed (EC) conditions. In addition to EEG measurements, we assessed clinical and psychological symptoms and serum levels of brain-derived neurotrophic factor (BDNF). The connectivity between eight ROIs was computed across eight different EEG frequencies.

Results

The FM group demonstrated increased connectivity between the left dorsolateral prefrontal cortex (DLPFC) and right anterior cingulate cortex (ACC), specifically in the beta-3 frequency band (t = 3.441, p = 0.044). When comparing the EO and EC conditions, FM patients exhibited heightened interhemispheric connectivity between insular areas (t  = 3.372, p = 0.024) and between the left insula (INS) and right DLPFC (t = 3.695, p = 0.024) within the beta-3 frequency band. In the EC condition, there was a negative correlation between pain disability and connectivity in the beta-3 frequency band between the left ACC and the left primary somatosensory cortex (SI; r = −0.442, p = 0.043). In the EO condition, there was a negative correlation between central sensitization severity and lagged coherence connectivity in the alpha-2 frequency band between the right ACC and left SI (r = 0.428, p = 0.014). Moreover, in the EO–EC comparison, the lagged coherence connection between the left DLPFC and right INS, indexed by the gamma frequency band, showed a negative correlation with serum BDNF levels (r = −0.506, p = 0.012).

Conclusion

These findings indicate that increased connectivity between different pain processing circuits, particularly in the beta-3 frequency band during rest, may serve as neural biomarkers for the chronic pain brain signature associated with neuroplasticity and the severity of FM symptoms.
Case Report: Post-traumatic splenosis and potential pitfall for PSMA-PET
Authors: Nearchou Marina , Georgiou Elizabeth , Vrachimis Alexis , Ferentinos Konstantinos , Strouthos Iosif
Published: November 2023

Background

18F-prostate specific membrane antigen (PSMA) PET is fast becoming the gold-standard in prostate cancer, both in staging of intermediate-/high-risk patients and in re-staging patients with biochemical failure. Several pitfalls of 18F-PSMA PET have been reported, and we report, to our best of knowledge, for the first time, a case which could have been falsely diagnosed as peritoneal spread.

Case presentation

A 67-year-old patient with high-risk prostate cancer underwent staging with 18F-PSMA-1007 PET/CT (PSMA-PET/CT). PSMA-PET/CT revealed a histologically confirmed prostatic malignancy in the peripheral left zone. Unexpectedly, additional multiple highly PSMA-expressing intraabdominal formations were discovered. Based on apparent anatomic asplenia and a history of traumatic splenic rapture during childhood, a suspicion of post-traumatic splenosis was raised. For further non-invasive evaluation, a C-99 sulphur colloid scintigraphy with SPECT was conducted, confirming the presence of multiple functional ectopic splenic tissues. This is, to our best of knowledge, the first case utilising 18F-PSMA-1007-PET/CT and 99mTc-sulphur colloid SPECT to detect intraabdominal splenosis, highlighting the high potential of nuclear medicine in such trivial cases.
Multi-dimensional role of gangliosides in modulating cancer hallmarks and their prospects in targeted cancer therapy
Authors: Sarkar Abhisek , Banerjee Sounak , Biswas Kaushik
Published: November 2023

Gangliosides are glycosphingolipids with prevalence in nervous tissue and their involvement in certain neuronal diseases have been widely known. Interestingly, many recent studies highlighted their importance in the development and progression of various cancers through orchestration of multiple attributes of tumorigenesis, i.e., promoting migration, invasion, escaping the host immune system, and influencing other cancer hallmarks. Therefore, the multidimensional role of gangliosides in different cancers has established them as potential cancer targets. However, the tremendous structural complexity and functional heterogeneity are the major challenges in ganglioside research. Moreover, despite numerous immunotherapeutic attempts to target different gangliosides, it has failed to yield consistent results in clinical trials owing to their poor immunogenicity, a broad range of cross-reactivity, severe side effects, lack of uniform expression as well as heterogeneity. The recent identification of selective O-acetylated ganglioside expression in cancer tissues, but not in normal tissues, has strengthened their potential as a better and specific target for treating cancer patients. It was further supported by reduced cross-reactivity and side effects in clinical trials, although poor immunogenicity remains a major concern. Therefore, in addition to characterization and identification of the biological importance of O-acetylated gangliosides, their specific and efficient targeting in cancer through engineered antibodies is an emerging area of glycobiology research. This review highlights the modulatory effect of select gangliosides on different hallmarks of cancer and presents the overall development of ganglioside targeted immunotherapies along with recent progress. Here, we have also discussed its potential for future modifications aimed towards improvement in ganglioside-based cancer therapies.
Protective effects of Salvianic acid A against multiple-organ ischemia-reperfusion injury: a review
Authors: Yang Shiyu , Chen Heming , Su Wei , Luo Yunchun , Liao Jun , Wang Yun , Xiong Liyan , Zhang Chuan , Li Fei , Chen Zhe-Sheng , Wang Tingfang
Published: November 2023

Ischemia-reperfusion (I/R) injury refers to a new injury caused by reperfusion after the restoration of ischemic tissue or organ blood supply. Salvianic acid A (danshensu) is a primary active ingredient extracted from Salvia miltiorrhiza. It has a protective function against I/R injury in the cardiovascular system, brain, liver, kidney, gastrointestinal tract, and other organs. This article reviews evidence of the protective effects of Salvianic acid A and its potential mechanisms of action in organ I/R injury protection. The aim of this review is to investigate the role of Salvianic acid A in the treatment of I/R injury, providing a reference resource that could facilitate subsequent studies.
Verapamil chronicles: advances from cardiovascular to pancreatic β-cell protection
Authors: Arefanian Hossein , Koti Lubaina , Sindhu Sardar , Ahmad Rasheed , Al Madhoun Ashraf , Al-Mulla Fahd
Published: November 2023

Verapamil is a well-known drug used for treating angina and hypertension. Emerging data from current clinical trials suggest that this calcium channel blocker has a potential benefit for pancreatic β-cells through the elevation and sustenance of C-peptide levels in patients with diabetes mellitus (DM). This is intriguing, given the fact that the current therapeutic options for DM are still limited to using insulin and incretins which, in fact, fail to address the underlying pathology of β-cell destruction and loss. Moreover, verapamil is widely available as an FDA-approved, cost-effective drug, supported also by its substantial efficacy and safety. However, the molecular mechanisms underlying the β-cell protective potentials of verapamil are yet to be fully elucidated. Although, verapamil reduces the expression of thioredoxin-interacting protein (TXNIP), a molecule which is involved in β-cell apoptosis and glucotoxicity-induced β-cell death, other signaling pathways are also modulated by verapamil. In this review, we revisit the historical avenues that lead to verapamil as a potential therapeutic agent for DM. Importantly, this review provides an update on the current known mechanisms of action of verapamil and also allude to the plausible mechanisms that could be implicated in its β-cell protective effects, based on our own research findings.
Is olive crop modelling ready to assess the impacts of global change?
Authors: Villalobos Francisco J. , López-Bernal Álvaro , García-Tejera Omar , Testi Luca
Published: November 2023

Olive trees, alongside grapevines, dominate the Mediterranean tree crop landscape. However, as climate change intensifies, the Mediterranean region, which encompasses 95% of the global olive cultivation area, faces significant challenges. Rising carbon dioxide (CO2) levels, increasing temperatures, and declining precipitation pose substantial threats to olive tree performance. Photosynthesis, respiration, phenology, water use and ultimately yield are possibly the main factors affected. To address this future scenario, it is crucial to develop adaptation and mitigation strategies. Nevertheless, breeding programs and field management practice testing for tree crops are time-consuming endeavors. Fortunately, models can accelerate the evaluation of tailored solutions. In this review, we critically examine the current state of olive tree modeling and highlight key areas requiring improvement. Given the expected impact of climate change, prioritizing research on phenology, particularly regarding bloom and pollination, is essential. Simulations of biomass should incorporate approaches that account for the interactive effects of CO2 and temperature on photosynthesis and respiration. Furthermore, accurately simulating the influence of water stress on yield necessitates the development of models that integrate canopy behavior with root performance under conditions of water scarcity. By addressing these critical aspects, olive tree models can enhance our understanding of climate change impacts and inform sustainable agricultural practices.
Integrative transcriptomics and proteomics profiling of Arabidopsis thaliana elucidates novel mechanisms underlying spaceflight adaptation
Authors: Olanrewaju Gbolaga O. , Haveman Natasha J. , Naldrett Michael J. , Paul Anna-Lisa , Ferl Robert J. , Wyatt Sarah E.
Published: November 2023

Spaceflight presents a unique environment with complex stressors, including microgravity and radiation, that can influence plant physiology at molecular levels. Combining transcriptomics and proteomics approaches, this research gives insights into the coordination of transcriptome and proteome in Arabidopsis’ molecular and physiological responses to Spaceflight environmental stress. Arabidopsis seedlings were germinated and grown in microgravity (µg) aboard the International Space Station (ISS) in NASA Biological Research in Canisters – Light Emitting Diode (BRIC LED) hardware, with the ground control established on Earth. At 10 days old, seedlings were frozen in RNA-later and returned to Earth. RNA-seq transcriptomics and TMT-labeled LC-MS/MS proteomic analysis of cellular fractionates from the plant tissues suggest the alteration of the photosynthetic machinery (PSII and PSI) in spaceflight, with the plant shifting photosystem core-regulatory proteins in an organ-specific manner to adapt to the microgravity environment. An overview of the ribosome, spliceosome, and proteasome activities in spaceflight revealed a significant abundance of transcripts and proteins involved in protease binding, nuclease activities, and mRNA binding in spaceflight, while those involved in tRNA binding, exoribonuclease activity, and RNA helicase activity were less abundant in spaceflight. CELLULOSE SYNTHASES (CESA1, CESA3, CESA5, CESA7) and CELLULOSE-LIKE PROTEINS (CSLE1, CSLG3), involved in cellulose deposition and TUBULIN COFACTOR B (TFCB) had reduced abundance in spaceflight. This contrasts with the increased expression of UDP-ARABINOPYRANOSE MUTASEs, involved in the biosynthesis of cell wall non-cellulosic polysaccharides, in spaceflight. Both transcripts and proteome suggested an altered polar auxin redistribution, lipid, and ionic intracellular transportation in spaceflight. Analyses also suggest an increased metabolic energy requirement for plants in Space than on Earth, hence, the activation of several shunt metabolic pathways. This study provides novel insights, based on integrated RNA and protein data, on how plants adapt to the spaceflight environment and it is a step further at achieving sustainable crop production in Space.
AC-UNet: an improved UNet-based method for stem and leaf segmentation in Betula luminifera
Authors: Yi Xiaomei , Wang Jiaoping , Wu Peng , Wang Guoying , Mo Lufeng , Lou Xiongwei , Liang Hao , Huang Huahong , Lin Erpei , Maponde Brian Tapiwanashe , Lv Chaihui
Published: November 2023

Plant phenotypic traits play an important role in understanding plant growth dynamics and complex genetic traits. In phenotyping, the segmentation of plant organs, such as leaves and stems, helps in automatically monitoring growth and improving screening efficiency for large-scale genetic breeding. In this paper, we propose an AC-UNet stem and leaf segmentation algorithm based on an improved UNet. This algorithm aims to address the issues of feature edge information loss and sample breakage in the segmentation of plant organs, specifically in Betula luminifera. The method replaces the backbone feature extraction network of UNet with VGG16 to reduce the redundancy of network information. It adds a multi-scale mechanism in the splicing part, an optimized hollow space pyramid pooling module, and a cross-attention mechanism in the expanding network part at the output end to obtain deeper feature information. Additionally, Dice_Boundary is introduced as a loss function in the back-end of the algorithm to circumvent the sample distribution imbalance problem. The PSPNet model achieves mIoU of 58.76%, mPA of 73.24%, and Precision of 66.90%, the DeepLabV3 model achieves mIoU of 82.13%, mPA of 91.47%, and Precision of 87.73%, on the data set. The traditional UNet model achieves mIoU of 84.45%, mPA of 91.11%, and Precision of 90.63%, and the Swin-UNet model achieves . The mIoU is 79.02%, mPA is 85.99%, and Precision is 88.73%. The AC-UNet proposed in this article achieved excellent performance on the Swin-UNet dataset, with mIoU, mPA, and Precision of 87.50%, 92.71%, and 93.69% respectively, which are better than the selected PSPNet, DeepLabV3, traditional UNet, and Swin-UNet. Commonly used semantic segmentation algorithms. Experiments show that the algorithm in this paper can not only achieve efficient segmentation of the stem and leaves of Betula luminifera but also outperforms the existing state-of-the-art algorithms in terms of both speed. This can provide more accurate auxiliary support for the subsequent acquisition of plant phenotypic traits.
Eating behavior of adolescent girls in countries with a high prevalence of stunting under five: a systematic review
Authors: Setiawan Arlette Suzy , Budiarto Arief , Indriyanti Ratna
Published: November 2023

Background

Adolescence is critical for physical and psychological growth, making healthy dietary behavior essential. Several countries face nutritional challenges due to a high prevalence of stunting in children under five, which can impact adolescent girls’ eating behavior. This systematic review aims to provide evidence on the eating behavior of adolescent girls in such countries, offering insights for stunting prevention programs.

Methods

We conducted a systematic review following PRISMA guidelines and registered it in PROSPERO. We employed a comprehensive search strategy across multiple databases. Study selection involved three stages: deduplication, title/abstract screening, and full-text review, with inter-reviewer agreement assessment.

Results

The search identified 15 eligible studies from various countries, primarily utilizing cross-sectional designs. The studies focused on age ranges within adolescence and varied in sample size and assessment tools. The primary objective of these studies was to assess eating behavior, with outcomes related to dietary patterns, eating disorders, body image, and nutritional knowledge. The results revealed diverse eating behaviors among adolescent girls, including restrained eating, dieting for weight loss, craving-induced eating, and unhealthy dietary patterns. Many girls exhibited low dietary diversity, contributing to micronutrient deficiencies.

Suggestions

To address these issues effectively, stunting prevention programs and policies should prioritize the following strategies: implementing comprehensive nutritional education programs to enhance adolescents’ knowledge of healthy eating habits and dietary diversity, providing resources and support for positive body image development to reduce the pressure for unhealthy dieting, improving access to affordable, nutrient-rich foods in both urban and rural areas, raising awareness about eating disorders and emphasizing early identification and intervention, engaging parents, caregivers, and communities in promoting healthy eating behaviors, and maintaining rigorous research and monitoring to adapt strategies based on evolving trends in adolescent eating behaviors.

Conclusion

Adolescent girls in countries with high stunting prevalence exhibit diverse eating behaviors that can impact their nutritional status and overall health. Addressing these behaviors is essential to prevent stunting and its long-term consequences, emphasizing the importance of comprehensive strategies and support for this vulnerable population.

Systematic review registration

https://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023389909.
Impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life of Australians: a population-based longitudinal data analysis
Authors: Mi Jun , Ishida Marie , Anindya Kanya , McPake Barbara , Fitzgibbon Bernadette , Laverty Anthony A. , Tran-Duy An , Lee John Tayu
Published: November 2023

Background

Health risk factors, including smoking, excessive alcohol consumption, overweight, obesity, and insufficient physical activity, are major contributors to many poor health conditions. This study aimed to assess the impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life (HRQoL) in Australia.

Methods

We used two waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) Survey from 2013 and 2017 for the analysis. Healthcare resource utilization included outpatient visits, hospitalisations, and prescribed medication use. Work-related outcomes were assessed through employment status and sick leave. HRQoL was assessed using the SF-6D scores. Generalized estimating equation (GEE) with logit or log link function and random-effects regression models were used to analyse the longitudinal data on the relationship between health risk factors and the outcomes. The models were adjusted for age, sex, marital status, education background, employment status, equilibrium household income, residential area, country of birth, indigenous status, and socio-economic status.

Results

After adjusting for all other health risk factors covariates, physical inactivity had the greatest impact on healthcare resource utilization, work-related outcomes, and HRQoL. Physical inactivity increased the likelihood of outpatient visits (AOR = 1.60, 95% CI = 1.45, 1.76 p < 0.001), hospitalization (AOR = 1.83, 95% CI = 1.66–2.01, p < 0.001), and the probability of taking sick leave (AOR = 1.31, 95% CI = 1.21–1.41, p < 0.001), and decreased the odds of having an above population median HRQoL (AOR = 0.48, 95% CI = 0.45–0.51, p < 0.001) after adjusting for all other health risk factors and covariates. Obesity had the greatest impact on medication use (AOR = 2.02, 95% CI = 1.97–2.29, p < 0.001) after adjusting for all other health risk factors and covariates.

Conclusion

Our study contributed to the growing body of literature on the relative impact of health risk factors for healthcare resource utilization, work-related outcomes and HRQoL. Our results suggested that public health interventions aim at improving these risk factors, particularly physical inactivity and obesity, can offer substantial benefits, not only for healthcare resource utilization but also for productivity.
The leading global health challenges in the artificial intelligence era
Authors: Zaidan Amal Mousa
Published: November 2023

Millions of people’s health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the “digital revolution,” characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
Depression, anxiety, and burnout in academia: topic modeling of PubMed abstracts
Authors: Lezhnina Olga
Published: November 2023

The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975–2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research.
Examining the barriers and recommendations for integrating more equitable insomnia treatment options in primary care
Authors: Vargas Ivan , Egeler Mara , Walker Jamie , Benitez Dulce Diaz
Published: November 2023

Chronic insomnia is the most common sleep disorder, occurring in ~32 million people in the United States per annum. Acute insomnia is even more prevalent, affecting nearly half of adults at some point each year. The prevalence of insomnia among primary care patients is even higher. The problem, however, is that most primary care providers do not feel adequately knowledgeable or equipped to treat sleep-related concerns. Many providers have never heard of or have not been trained in cognitive behavioral therapy for insomnia or CBT-I (the first line treatment for insomnia). The focus of the current review is to summarize the factors contributing to why sleep health and insomnia treatment have been mostly neglected, identify how this has contributed to disparities in sleep health among certain groups, particularly racial and ethnic minorities and discuss considerations or potential areas of exploration that may improve access to behavioral sleep health interventions, particularly in primary care.
Host cellular factors involved in pseudorabies virus attachment and entry: a mini review
Authors: Tan Lei , Wang Kaixin , Bai Ping , Zhang Shuo , Zuo Mengting , Shu Xianghua , Wang Aibing , Yao Jun
Published: November 2023

Pseudorabies virus (PRV) belongs to the Alphaherpesvirinae subfamily and serves as an exceptional animal model for investigating the infection mechanism of Herpes simplex virus type 1. Notably, PRV has the capability to infect a wide range of mammals, including humans, highlighting its potential as an overlooked zoonotic pathogen. The attachment and entry steps of PRV into host cells are crucial to accomplish its life cycle, which involve numerous cellular factors. In this mini review, we offer a comprehensive summary of current researches pertaining to the role of cellular factors in PRV attachment and entry stages, with the overarching goal of advancing the development of novel antiviral agents against this pathogen.
NDARTS: A Differentiable Architecture Search Based on the Neumann Series
Authors: Han Xiaoyu , Li Chenyu , Wang Zifan , Liu Guohua
Published: November 2023

Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML). Although search methods based on reinforcement learning and evolutionary algorithms can find high-performance architectures, these search methods typically require hundreds of GPU days. Unlike searching in a discrete search space based on reinforcement learning and evolutionary algorithms, the differentiable neural architecture search (DARTS) continuously relaxes the search space, allowing for optimization using gradient-based methods. Based on DARTS, we propose NDARTS in this article. The new algorithm uses the Implicit Function Theorem and the Neumann series to approximate the hyper-gradient, which obtains better results than DARTS. In the simulation experiment, an ablation experiment was carried out to study the influence of the different parameters on the NDARTS algorithm and to determine the optimal weight, then the best performance of the NDARTS algorithm was searched for in the DARTS search space and the NAS-BENCH-201 search space. Compared with other NAS algorithms, the results showed that NDARTS achieved excellent results on the CIFAR-10, CIFAR-100, and ImageNet datasets, and was an effective neural architecture search algorithm.
Comparing Activation Functions in Machine Learning for Finite Element Simulations in Thermomechanical Forming
Authors: Pantalé Olivier
Published: November 2023

Finite element (FE) simulations have been effective in simulating thermomechanical forming processes, yet challenges arise when applying them to new materials due to nonlinear behaviors. To address this, machine learning techniques and artificial neural networks play an increasingly vital role in developing complex models. This paper presents an innovative approach to parameter identification in flow laws, utilizing an artificial neural network that learns directly from test data and automatically generates a Fortran subroutine for the Abaqus standard or explicit FE codes. We investigate the impact of activation functions on prediction and computational efficiency by comparing Sigmoid, Tanh, ReLU, Swish, Softplus, and the less common Exponential function. Despite its infrequent use, the Exponential function demonstrates noteworthy performance and reduced computation times. Model validation involves comparing predictive capabilities with experimental data from compression tests, and numerical simulations confirm the numerical implementation in the Abaqus explicit FE code.
Enhancing Cryptocurrency Price Forecasting by Integrating Machine Learning with Social Media and Market Data
Authors: Belcastro Loris , Carbone Domenico , Cosentino Cristian , Marozzo Fabrizio , Trunfio Paolo
Published: November 2023

Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.
Genomic and Transcriptomic Approaches to Developing Abiotic Stress-Resilient Crops
Authors: Kamali Saravanappriyan , Singh Amarjeet
Published: November 2023

In the realm of agriculture, a pressing concern remains the abiotic stresses, such as temperature fluctuation, drought, soil salinity, and heavy metal contamination. These adverse growth conditions hamper crop yields and global food security. In this review, we present a comprehensive examination of the recent advancements in utilizing genomics and transcriptomics, tools to enhance crop resilience against these stress factors. Genomics aids in the identification of genes responsive to stress, unravels regulatory networks, and pinpoints genetic variations linked to stress tolerance. Concurrently, transcriptomics sheds light on the intricate dynamics of gene expression during stress conditions, unearthing novel stress-responsive genes and signaling pathways. This wealth of knowledge shapes the development of stress-tolerant crop varieties, achieved through conventional breeding programs and state-of-the-art genetic engineering and gene editing techniques like CRISPR-Cas9. Moreover, the integration of diverse omics data and functional genomics tools empowers precise manipulation of crop genomes to fortify their stress resilience. In summary, the integration of genomics and transcriptomics holds substantial promise in elucidating the molecular mechanisms behind crop stress tolerance, offering a path towards sustainable agriculture and safeguarding food security amidst shifting environmental challenges.