bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gender-based violence in the context of armed conflict in Northern Ethiopia
Authors: Tewabe Desalew Salew , Azage Muluken , Wubetu Gizachew Yismaw , Fenta Sisay Awoke , Worke Mulugeta Dile , Asres Amanu Mekonen , Getnet Wallelign Alemnew , Kassie Genet Gedamu , Menber Yonatan , Munea Alemtsehay Mekonnen , Zeru Taye , Bekele Selamawit Alemayehu , Abdulahi Sadiya Osman , Adamne Tigist Biru , Belete Hiwot Debebe , Beyene Belay Bezabih , Abte Melkamu , Mersha Tesfaye B , Dadi Abel Fekadu , Enquobahrie Daniel A , Frissa Souci M. , Geda Yonas E. 
Published: January 2024

Abstract
Background
Gender-based violence (GBV) particularly against women is unfortunately common during armed conflicts. No rigorous and comprehensive empirical work has documented the extent of GBV and its consequences that took place during the two years of devastating armed conflict in Northern Ethiopia. This study aims to assess GBV and its consequences in war-torn areas of northern Ethiopia.

Methods
We used a qualitative method augmented by quantitative method to enroll research participants. We conducted in-depth interviews to characterize the lived experiences of GBV survivors. All interviews were conducted confidentially. The data were collected to the point of data saturation. All interviews were transcribed verbatim into local language, translated into English, and analyzed using a thematic analysis approach. We also used reports from healthcare facilities and conducted a descriptive analysis of the demographic characteristics of study participants.

Results
One thousand one hundred seventy-seven persons reported GBV to healthcare providers. The qualitative study identified several forms of violence (sexual, physical, and psychological). Gang rape against women including minors as young as 14 years old girls was reported. Additionally, the perpetrators sexually violated women who were pregnant, and elderly women as old as 65 years, who took refuge in religious institutions. The perpetrators committed direct assaults on the body with items (e.g., burning the body with cigarette fire) or weapons, holding women and girls as captives, and deprivation of sleep and food. GBV survivors reported stigma, prejudice, suicide attempts, nightmares, and hopelessness. GBV survivors dealt with the traumatic stress by outmigration (leaving their residences), seeking care at healthcare facilities, self-isolation, being silent, dropping out of school, and seeking counseling.

Conclusion
GBV survivors were subjected to multiple and compounding types of violence, with a wide range of adverse health consequences for survivors and their families. GBV survivors require multifaceted interventions including psychological, health, and economic support to rehabilitate them to lead a productive life.
Physical activity and mental health in school-aged children: a prospective two-wave study during the easing of the COVID-19 restrictions
Authors: Kopp Philipp M. , Möhler Eva , Gröpel Peter 
Published: January 2024

Abstract
Background
Because physical activity (PA) has many benefits for children’s and adolescents’ mental health, it has been suggested that PA may be an effective strategy to physically and mentally recover from the COVID-19 pandemic. This study tested the reciprocal relationship between PA and mental health during the easing of COVID-19 restrictions. It was hypothesized that mental health during the pandemic would determine how much children and adolescents re-engage in PA after easing the restrictions. Furthermore, it was hypothesized that PA engagement would predict mental health improvement after the pandemic.

Methods
This was a prospective study with two measurement occasions. Pretest data collection was undertaken in February 2022, shortly before German authorities eased and lifted the COVID-19 restrictions. The follow-up (posttest) occurred six weeks later (April 2022). Both times, a sample of elementary and grammar school students aged 6 to 18 years (N = 170) reported their mental health problems and health-related quality of life. Mental health problems were assessed with the Strengths and Difficulties Questionnaire and health-related quality of life was assessed with the KIDSCREEN-52 questionnaire. PA was measured continuously during the study period using smart electronic devices with a built-in pedometer.

Results
PA gradually increased after the easing of COVID-19 restrictions (p < .001). This increase was unrelated to pretest mental health problems and health-related quality of life except for emotional symptoms (p = .041). Participants with higher emotional symptoms showed a sharper increase in PA towards the end of the study period. Furthermore, hyperactivity decreased (p = .004) and physical well-being (p = .004), perceived autonomy (p < .001), and perceived quality of school environment (p = .008) improved from before to after the easing of restrictions, yet participants’ PA predicted none of these changes.

Conclusions
The adverse effects of COVID-19 containment on PA seem to alleviate after children and adolescents are allowed to return to schools. This is likely to be due to the school setting, which provides many different opportunities for formal and informal PA rather than students’ mental health. School-related PA programs should be part of children’s and adolescents’ recovery from the pandemic .
NK cell-triggered CCL5/IFNγ-CXCL9/10 axis underlies the clinical efficacy of neoadjuvant anti-HER2 antibodies in breast cancer
Authors: Santana-Hernández Sara , Suarez-Olmos Jesús , Servitja Sonia , Berenguer-Molins Pau , Costa-Garcia Marcel , Comerma Laura , Rea Anna , Perera-Bel Julia , Menendez Silvia , Arpí Oriol , Bermejo Begoña , Martínez María Teresa , Cejalvo Juan Miguel , Comino-Méndez Iñaki , Pascual Javier , Alba Emilio , López-Botet Miguel , Rojo Federico , Rovira Ana , Albanell Joan , Muntasell Aura 
Published: January 2024

Abstract
Background
The variability in responses to neoadjuvant treatment with anti-HER2 antibodies prompts to personalized clinical management and the development of innovative treatment strategies. Tumor-infiltrating Natural Killer (TI-NK) cells can predict the efficacy of HER2-targeted antibodies independently from clinicopathological factors in primary HER2-positive breast cancer patients. Understanding the mechanism/s underlying this association would contribute to optimizing patient stratification and provide the rationale for combinatorial approaches with immunotherapy.

Methods
We sought to uncover processes enriched in NK cell-infiltrated tumors as compared to NK cell-desert tumors by microarray analysis. Findings were validated in clinical trial-derived transcriptomic data. In vitro and in vivo preclinical models were used for mechanistic studies. Findings were analysed in clinical samples (tumor and serum) from breast cancer patients.

Results
NK cell-infiltrated tumors were enriched in CCL5/IFNG-CXCL9/10 transcripts. In multivariate logistic regression analysis, IFNG levels underlie the association between TI-NK cells and pathological complete response to neoadjuvant treatment with trastuzumab. Mechanistically, the production of IFN-ɣ by CD16+ NK cells triggered the secretion of CXCL9/10 from cancer cells. This effect was associated to tumor growth control and the conversion of CD16 into CD16-CD103+ NK cells in humanized in vivo models. In human breast tumors, the CD16 and CD103 markers identified lineage-related NK cell subpopulations capable of producing CCL5 and IFN-ɣ, which correlated with tissue-resident CD8+ T cells. Finally, an early increase in serum CCL5/CXCL9 levels identified patients with NK cell-rich tumors showing good responses to anti-HER2 antibody-based neoadjuvant treatment.

Conclusions
This study identifies specialized NK cell subsets as the source of IFN-ɣ influencing the clinical efficacy of anti-HER2 antibodies. It also reveals the potential of serum CCL5/CXCL9 as biomarkers for identifying patients with NK cell-rich tumors and favorable responses to anti-HER2 antibody-based neoadjuvant treatment.
Synthesis, design, biological evaluation, and computational analysis of some novel uracil-azole derivatives as cytotoxic agents
Authors: Emami Leila , Zare Fateme , Khabnadideh Soghra , Rezaei Zahra , Sabahi Zahra , Zare Gheshlaghi Saman , Behrouz Marzieh , Emami Mina , Ghobadi Zahra , Madadelahi Ardekani Sedighe , Barzegar Fatemeh , Ebrahimi Ali , Sabet Razieh 
Published: January 2024

AbstractThe design and synthesis of novel cytotoxic agents is still an interesting topic for medicinal chemistry researchers due to the unwanted side effects of anticancer drugs. In this study, a novel series of uracil–azole hybrids were designed and synthesized. The cytotoxic activity, along with computational studies: molecular docking, molecular dynamic simulation, density functional theory, and ADME properties were also, evaluated. The compounds were synthesized by using 3-methyl-6-chlorouracil as the starting material. Cytotoxicity was determined using MTT assay in the breast carcinoma cell line (MCF-7) and Hepatocellular carcinoma cell line (HEPG-2). These derivatives demonstrated powerful inhibitory activity against breast and hepatocellular carcinoma cell lines in comparison to Cisplatin as positive control. Among these compounds, 4j displayed the best selectivity profile and good activity with IC50 values of 16.18 ± 1.02 and 7.56 ± 5.28 µM against MCF-7 and HEPG-2 cell lines respectively. Structure–activity relationships revealed that the variation in the cytotoxic potency of the synthesized compounds was affected by various substitutions of benzyl moiety. The docking output showed that 4j bind well in the active site of EGFR and formed a stable complex with the EGFR protein. DFT was used to investigate the reactivity descriptors of 4a and 4j. The outputs demonstrated that these uracil–azole hybrids can be considered as potential cytotoxic agents.
Quality of spirometry tests in the field of occupational health
Authors: Alavi Foumani Amirala , Alavi Foumani Seyyed Ali , Attarchi Mirsaeed , Etemadi Deilami Alireza , Majlesi Behzad , Ildari Shima , Eslami-Kenarsari Habib 
Published: January 2024

Abstract
Background
The spirometry test is a valuable test to evaluate the performance of the respiratory system. The interpretation of the results is highly dependent on the quality of its performance, while the inappropriate quality results in unwanted consequences for individuals and the healthcare system. This study investigated the quality of spirometry tests performed in occupational health.

Methods
In this cross-sectional study, the quality of 776 spirometry tests in different occupational centers by the specialists in Rasht, Iran, in 2020, based on the guidelines of the American Thoracic Society (ATS), was investigated. The quality and success rate of the test and the demographical characteristics of the operators and the participants were collected. All data was analyzed using SPSS software version 20.

Results
Out of 776 spirometry tests, about 69.7% were unacceptable. Among the unacceptable tests, a pause error between inhalation and exhalation was identified in 7.4% of tests. Additionally, 4.6% of the unacceptable tests exhibited a cough error within the first second, while an exhalation error of less than six was observed in 85%. Repeatability errors were found in 60.9% of the tests. Furthermore, among some errors, the communication error between the characteristics of the technicians and the test performance errors were evident.

Conclusion
According to the results, most of the performed tests were unacceptable with no repeatability, which indicated that the validity and quality of spirometry tests and their interpretation were inappropriate in the field of occupational health in Rasht, Iran.
High-fat Western diet alters crystalline silica-induced airway epithelium ion transport but not airway smooth muscle reactivity
Authors: Thompson Janet A. , Kashon Michael L. , McKinney Walter , Fedan Jeffrey S. 
Published: January 2024

Abstract
Objectives
Silicosis is an irreversible occupational lung disease resulting from crystalline silica inhalation. Previously, we discovered that Western diet (HFWD)-consumption increases susceptibility to silica-induced pulmonary inflammation and fibrosis. This study investigated the potential of HFWD to alter silica-induced effects on airway epithelial ion transport and smooth muscle reactivity.

Methods
Six-week-old male F344 rats were fed a HFWD or standard rat chow (STD) and exposed to silica (Min-U-Sil 5®, 15 mg/m3, 6 h/day, 5 days/week, for 39 d) or filtered air. Experimental endpoints were measured at 0, 4, and 8 weeks post-exposure. Transepithelial potential difference (Vt), short-circuit current (ISC) and transepithelial resistance (Rt) were measured in tracheal segments and ion transport inhibitors [amiloride, Na+ channel blocker; NPPB; Clˉ channel blocker; ouabain, Na+, K+-pump blocker] identified changes in ion transport pathways. Changes in airway smooth muscle reactivity to methacholine (MCh) were investigated in the isolated perfused trachea preparation.

Results
Silica reduced basal ISC at 4 weeks and HFWD reduced the ISC response to amiloride at 0 week compared to air control. HFWD + silica exposure induced changes in ion transport 0 and 4 weeks after treatment compared to silica or HFWD treatments alone. No effects on airway smooth muscle reactivity to MCh were observed.
MC180295 is a highly potent and selective CDK9 inhibitor with preclinical in vitro and in vivo efficacy in cancer
Authors: Zhang Hanghang , Huang Chen , Gordon John , Yu Sijia , Morton George , Childers Wayne , Abou-Gharbia Magid , Zhang Yi , Jelinek Jaroslav , Issa Jean-Pierre J. 
Published: January 2024

Abstract
Background
Inhibition of cyclin-dependent kinase 9 (CDK9), a novel epigenetic target in cancer, can reactivate epigenetically silenced genes in cancer by dephosphorylating the SWI/SNF chromatin remodeler BRG1. Here, we characterized the anti-tumor efficacy of MC180295, a newly developed CDK9 inhibitor.

Methods
In this study, we explored the pharmacokinetics of MC180295 in mice and rats, and tested the anti-tumor efficacy of MC180295, and its enantiomers, in multiple cancer cell lines and mouse models. We also combined CDK9 inhibition with a DNA methyltransferase (DNMT) inhibitor, decitabine, in multiple mouse models, and tested MC180295 dependence on T cells. Drug toxicity was measured by checking body weights and complete blood counts.

Results
MC180295 had high specificity for CDK9 and high potency against multiple neoplastic cell lines (median IC50 of 171 nM in 46 cell lines representing 6 different malignancies), with the highest potency seen in AML cell lines derived from patients with MLL translocations. MC180295 is a racemic mixture of two enantiomers, MC180379 and MC180380, with MC180380 showing higher potency in a live-cell epigenetic assay. Both MC180295 and MC180380 showed efficacy in in vivo AML and colon cancer xenograft models, and significant synergy with decitabine in both cancer models. Lastly, we found that CDK9 inhibition-mediated anti-tumoral effects were partially dependent on CD8 + T cells in vivo, indicating a significant immune component to the response.

Conclusions
MC180380, an inhibitor of cyclin-dependent kinase 9 (CDK9), is an efficacious anti-cancer agent worth advancing further toward clinical use.
TET2-mediated ECM1 hypomethylation promotes the neovascularization in active proliferative diabetic retinopathy
Authors: Cai Chunyang , Gu Chufeng , He Shuai , Meng Chunren , Lai Dongwei , Zhang Jingfa , Qiu Qinghua 
Published: January 2024

Abstract
Background
Studies have shown that tet methylcytosine dioxygenase 2 (TET2) is highly expressed in diabetic retinopathy (DR), which reduces the DNA methylation of downstream gene promoters and activates the transcription. Abnormally expressed TET2 and downstream genes in a high-glucose environment are associated with retinal capillary leakage and neovascularization. Here, we investigated the downstream genes of TET2 and its potential association with neovascularization in proliferative diabetic retinopathy (PDR).

Methods
GSE60436, GSE57362, and GSE158333 datasets were analyzed to identify TET2-related hypomethylated and upregulated genes in PDR. Gene expression and promoter methylation of these genes under high glucose treatment were verified. Moreover, TET2 knockdown was used to assess its impact on tube formation and migration in human retinal microvascular endothelial cells (HRMECs), as well as its influence on downstream genes.

Results
Our analysis identified three key genes (PARVB, PTPRE, ECM1) that were closely associated with TET2 regulation. High glucose-treated HRMECs exhibited increased expression of TET2 and ECM1 while decreasing the promoter methylation level of ECM1. Subsequently, TET2 knockdown led to decreased migration ability and tube formation function of HRMECs. We further found a decreased expression of PARVB, PTPRE, and ECM1, accompanied by an increase in the promoter methylation of ECM1.

Conclusions
Our findings indicate the involvement of dysregulated TET2 expression in neovascularization by regulating the promoter methylation and transcription of downstream genes (notably ECM1), eventually leading to PDR. The TET2-induced hypomethylation of downstream gene promoters represents a potential therapeutic target and offers a novel perspective on the mechanism underlying neovascularization in PDR.

Graphical Abstract
Relative molecule self-attention transformer
Authors: Maziarka Łukasz , Majchrowski Dawid , Danel Tomasz , Gaiński Piotr , Tabor Jacek , Podolak Igor , Morkisz Paweł , Jastrzębski Stanisław 
Published: January 2024

AbstractThe prediction of molecular properties is a crucial aspect in drug discovery that can save a lot of money and time during the drug design process. The use of machine learning methods to predict molecular properties has become increasingly popular in recent years. Despite advancements in the field, several challenges remain that need to be addressed, like finding an optimal pre-training procedure to improve performance on small datasets, which are common in drug discovery. In our paper, we tackle these problems by introducing Relative Molecule Self-Attention Transformer for molecular representation learning. It is a novel architecture that uses relative self-attention and 3D molecular representation to capture the interactions between atoms and bonds that enrich the backbone model with domain-specific inductive biases. Furthermore, our two-step pretraining procedure allows us to tune only a few hyperparameter values to achieve good performance comparable with state-of-the-art models on a wide selection of downstream tasks.
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Authors: Wang Debby D. , Wu Wenhui , Wang Ran 
Published: January 2024

AbstractThe launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been generated. To compensate for the lack of valid comparisons among those models, we realized and evaluated representatives from a uniform basis, with the advantages and shortcomings discussed. This review will potentially benefit structure-based drug discovery and related areas.
Graphical Abstract
InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism
Authors: Lee Soyeon , Yoo Sunyong 
Published: January 2024

AbstractSafety is one of the important factors constraining the distribution of clinical drugs on the market. Drug-induced liver injury (DILI) is the leading cause of safety problems produced by drug side effects. Therefore, the DILI risk of approved drugs and potential drug candidates should be assessed. Currently, in vivo and in vitro methods are used to test DILI risk, but both methods are labor-intensive, time-consuming, and expensive. To overcome these problems, many in silico methods for DILI prediction have been suggested. Previous studies have shown that DILI prediction models can be utilized as prescreening tools, and they achieved a good performance. However, there are still limitations in interpreting the prediction results. Therefore, this study focused on interpreting the model prediction to analyze which features could potentially cause DILI. For this, five publicly available datasets were collected to train and test the model. Then, various machine learning methods were applied using substructure and physicochemical descriptors as inputs and the DILI label as the output. The interpretation of feature importance was analyzed by recognizing the following general-to-specific patterns: (i) identifying general important features of the overall DILI predictions, and (ii) highlighting specific molecular substructures which were highly related to the DILI prediction for each compound. The results indicated that the model not only captured the previously known properties to be related to DILI but also proposed a new DILI potential substructural of physicochemical properties. The models for the DILI prediction achieved an area under the receiver operating characteristic (AUROC) of 0.88–0.97 and an area under the Precision-Recall curve (AUPRC) of 0.81–0.95. From this, we hope the proposed models can help identify the potential DILI risk of drug candidates at an early stage and offer valuable insights for drug development.