bluuebunny/crossref_metadata_2025_split huggingface dataset:
A network meta-analysis of different acupuncture therapy in the treatment of poststroke cognitive impairment and dementia
Authors: Huo Lei , Zhao Manli , Wang Zeying , Zhang Lunzhong , Fu Kaili , Zhang Xuezheng 
Published: October 2024

Background:
Poststroke cognitive impairment and dementia (PSCID) is a major cause of stroke-related morbidities and mortalities. Over the last few years, there has been growing evidence supporting the effectiveness of needle-related treatments in PSCID. Our goal was to rate the included therapies and assess the clinical effectiveness of various needle-related treatments in patients with PSCID.

Methods:
We searched PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Chinese Biomedical Literature Service System (SinoMed), Wanfang, FDA.gov, and ClinicalTrials.gov. A mix of subject terms and free words was used to search the databases. The retrieval period was from the inception date of the database to February, 2023. We included SRs and MAs from acupuncture RCTs of patients with PSCID. The Cochrane Risk Assessment Scale was used to evaluate the risk of bias in the included studies. State 17.0 was used for network meta-analysis in accordance with the Bayesian framework.

Results:
There were 34 studies total of 2690 patients. The cumulative ranking curve (SUCRA) revealed that CT + CFT + EA was the most efficient intervention to improve (Mini-Mental State Examination, MMSE) efficiency, followed by CT + CFT + AP to improve (Montreal Cognitive Assessment, MoCA) efficiency, CT + CFT + ACU for improving (Activities of Daily Living scale, ADL) scores, and CT + CFT + EA to improve clinical efficiency.

Conclusion:
The results show that Different acupuncture methods can improve cognitive function and daily living ability in patients with PSCID. Network meta-analysis revealed that both CT + CFT + ACU and CT + CFT + EA appeared to be more beneficial for daily living activities, while CT + CFT + EA and CT + CFT + AP appeared to be more helpful for cognitive performance in patients with PSCID. Treatments including acupuncture are safer and have a reduced incidence of negative side effects.
Surgical outcomes of inverted internal limiting membrane flap technique for primary rhegmatogenous retinal detachment coexisting with a macular hole
Authors: Peng Kai-Ling , Kung Ya-Hsin , Wu Tsung-Tien 
Published: October 2024

This retrospective study aimed to evaluate the visual outcomes and foveal restoration in eyes with rhegmatogenous retinal detachment (RRD) and macular hole (MH) following surgery using the inverted internal limiting membrane (ILM) flap technique.
We collected data on eyes with coexisting RRD and MH that underwent surgery using the inverted ILM flap technique between January 2016 and December 2018. The primary outcome measures were visual acuity (VA) and optical coherence tomography findings.
Of 308 eyes with MH originating from various causes, 8 (2.6%) eyes diagnosed with coexisting RRD and MH treated using the inverted ILM flap technique were included. The average patient age and axial length were 65.00 ± 12.01 years and 23.66 ± 0.77 mm, respectively. The mean preoperative best-corrected VA (BCVA) was 1.84 (spherical equivalent [SE], counting finger) ± 0.32 logMAR and the mean final BCVA had improved significantly to 1.05 (SE, 20/223) ± 0.67 logMAR (P = .017). The primary retinal reattachment, MH closure, and secondary retinal attachment rates were 75% (6/8), 87.5% (7/8), and 100% (7/7), respectively.
The inverted ILM flap technique led to significant improvements in anatomical and visual outcomes in patients with coexisting RRD and MH.
A NOMOGRAM BASED ON THE VALUE OF THE DYNAMIC EVOLUTION OF SYSTEMIC IMMUNE INFLAMMATORY INDEX IN THE EVALUATION OF SEVERE HEATSTROKE
Authors: Xu Chongxiao , Yin Bingling , Zhao Yueli , Guo Haiyang , Hou Xiaogan , Liu Anwei , He Xuan , Chen Wenda , Lin Guodong , Pan Zhiguo 
Published: October 2024

ABSTRACT

Background: Severe heatstroke patients have a poor prognosis. There are few descriptions of the inflammatory response to heatstroke in clinical studies. Systemic immune-inflammation index (SII) is a new index to reflect the inflammatory state of disease. Methods: This retrospective observational study included patients who had severe heatstroke between 2010 and 2023. Multivariate logistic regression and nomogram were performed to determine the ability of the SII to predict the prognosis of these patients, and subgroup analysis was performed according to SII levels. Results: Of the 177 patients included in our study, 28 (15.8%) died. There was no difference in SII values between the first day (P = 0.810) and the second day (P = 0.184) in multivariate analysis. The SII value of the third day (SII 72) was elevated in patients with heatstroke who died compared to that in those who survived (P = 0.035). In multivariable logistic regression, Sequential Organ Failure Assessment (SOFA) score (odds ratio [OR], 1.717; confidence interval [CI], 1.073–2.747; P = 0.024) and SII 72 (OR, 1.001; 95% CI, 1.000–1.002; P = 0.035) were found to be independent predictors of mortality. SII 72 combined with SOFA score distinguished between patients who died and those who survived better than did the separate SOFA score. Patients with SII 72 > 1,000 had poor clinical prognosis. Conclusions: Compared to SII results from the first and second days, third-day results more meaningfully predict poor heatstroke prognosis. SII 72 may be a good indicator and, when combined with SOFA, offers enhanced predictive value.
PhenoScreen: A Dual-Space Contrastive Learning Framework-based Phenotypic Screening Method by Linking Chemical Perturbations to Cellular Morphology
Authors: Wang Shihang , Han Qilei , Qin Weichen , Wang Lin , Yuan Junhong , Zhao Yiqun , Ren Pengxuan , Zhang Yunze , Tang Yilin , Li Ruifeng , Li Zongquan , Zhang Wenchao , Gao Shenghua , Bai Fang 
Published: October 2024

AbstractPhenotypic drug discovery (PDD) screens compounds in cellular models that represent disease-relevant phenotypes, offering a compelling alternative to traditional target-based approaches. Unlike conventional methods, where compounds act on a single predefined target, PDD identifies compounds capable of exerting therapeutic effects through multiple targets and mechanisms. This makes PDD particularly valuable for discovering first-in-class drugs, especially for diseases with poorly understood molecular mechanisms or those lacking validated therapeutic targets. By enabling broader exploration of biological systems and uncovering multi-target drugs (polypharmacology), PDD provides a powerful strategy for tackling complex diseases. In this study, we introduce PhenoScreen, a deep learning framework designed to advance PDD by utilizing large-scale compound-phenotype association data. Through contrastive learning, PhenoScreen connects chemical space with cellular morphological profiles, allowing for accurate prediction of compound-induced phenotypic changes. PhenoScreen can also accurately identfiy lead compounds which could induce user defined phenotypic shift but more novel scaffolds using different levels of phenotypic information reflected by diverse compounds. The model was validated across multiple screening tasks and successfully predicted active compounds inducing user-specified phenotypes with varying inhibitory effects in the osteosarcoma phenotypic model. Further, other than showing effectiveness to osteosarcoma, our experiments also showed that PhenoScreen demonstrated strong generalization to rhabdomyosarcoma, and the active compound we screened had an IC50of up to 1.842 μM, suggesting its ability to capture key phenotypic features shared across cancer cells. These results underscore PhenoScreen’s potential to accelerate drug discovery by identifying novel therapeutic pathways and increasing the diversity of viable drug candidates. PhenoScreen is accessible online via our group’s web server for compound virtual screening athttps://bailab.siais.shanghaitech.edu.cn/services/PhenoScreen/, and the source codes are available athttps://github.com/Shihang-Wang-58/PhenoScreen.
scGenePT: Is language all you need for modeling single-cell perturbations?
Authors: Istrate Ana-Maria , Li Donghui , Karaletsos Theofanis 
Published: October 2024

AbstractModeling single-cell perturbations is a crucial task in the field of single-cell biology. Predicting the effect of up or down gene regulation or drug treatment on the gene expression profile of a cell can open avenues in understanding biological mechanisms and potentially treating disease. Most foundation models for single-cell biology learn from scRNA-seq counts, using experimental data as a modality to generate gene representations. Similarly, the scientific literature holds a plethora of information that can be used in generating gene representations using a different modality - language - as the basis. In this work, we study the effect of using both language and experimental data in modeling genes for perturbation prediction. We show that textual representations of genes provide additive and complementary value to gene representations learned from experimental data alone in predicting perturbation outcomes for single-cell data. We find that textual representations alone are not as powerful as biologically learned gene representations, but can serve as useful prior information. We show that different types of scientific knowledge represented as language induce different types of prior knowledge. For example, in the datasets we study, subcellular location helps the most for predicting the effect of single-gene perturbations, and protein information helps the most for modeling perturbation effects of interactions of combinations of genes. We validate our findings by extending the popular scGPT model, a foundation model trained on scRNA-seq counts, to incorporate language embeddings at the gene level. We start with NCBI gene card and UniProt protein summaries from the genePT approach and add gene function annotations from the Gene Ontology (GO). We name our model “scGenePT”, representing the combination of ideas from these two models. Our work sheds light on the value of integrating multiple sources of knowledge in modeling single-cell data, highlighting the effect of language in enhancing biological representations learned from experimental data.
Single-cell profiling of the lung immune cells of diabetes-tuberculosis comorbidity reveals reduced type-II interferon and elevated Th17 responses
Authors: Chaudhary Shweta , Sravya Mothe , Pahwa Falak , V Sureshkumar , Singh Prateek , Chaturvedi Shivam , Mohanty Debasisa , Dash Debasis , Nanda Ranjan Kumar 
Published: October 2024

AbstractUnderstanding the perturbed lung immune cells distribution and its functionality in tuberculosis (TB) is well documented; however, limited reports have covered their disruption, if any, in diabetes-tuberculosis (DM-TB) comorbid conditions. Here, we employed single-cell RNA-seq to investigate the molecular mechanisms that govern the heterogeneity in host immune response in DM-TB comorbid conditions. Diabetes is associated with chronic hyperinflammation and reduced lung-infiltrating immune cells, which delays the immune response to Mycobacterial infection. scRNA-seq of lung CD3⁺ and CD11c⁺ cells revealed compromised adaptive and innate immunity, with decreased Th1 and M1 macrophage populations in DM-TB mice. A dampened immune response, marked by increased IL-16 signaling and reduced TNF and IFN-II responses, was observed in DM-TB. This study highlights chronic inflammation, hyperglycemia, and dyslipidemia associated with diabetes impairing anti-TB immunity. Selective inhibition of aberrant IL-16 secretion and Th17 cell activation might provide strategies for better managing DM-TB comorbidity.
Integration of protein and coding sequences enables mutual augmentation of the language model
Authors: Zhao Heng-Rui , Cheng Meng-Ting , Zhu Jinhua , Wang Hao , Yang Xiang-Rui , Wang Bo , Sun Yuan-Xin , Fang Ming-Hao , Chen Enhong , Li Houqiang , Han Shu-Jing , Chen Yuxing , Zhou Cong-Zhao 
Published: October 2024

AbstractRecent language models have significantly accelerated our understanding on the massive biological data, using protein or DNA/RNA sequences as a single-language modality. Here we present a dual-language foundation model, which integrates both protein and coding sequences (CDS) for pre-training. Compared to the benchmark models, it shows a superior performance up to ∼20% on both protein and mRNA-related discriminative tasks, and gains the capacity to de novo generate coding sequences of ∼50% increased protein yield. Moreover, the model also possesses the knowledge transferability from the pre-training data to the upstream 5’ untranslated regions. These findings indicate the intrinsic correlations between protein and its CDS, as well as the coding region and beyond. It provides a new paradigm that leverages the multiple-language foundation model to interpret the hidden context of distinct corpora/biological languages, which could be further applied to mine the yet-unknown biological information/correlation beyond the Central Dogma.
Imaging of increased peritumoral glutamate and glutamine in gliomas using 7T MRSI
Authors: Hangel Gilbert , Lazen Philipp , Cadrien Cornelius , Chambers Stefanie , Furtner Julia , Hingerl Lukas , Strasser Bernhard , Kiesel Barbara , Mischkulnig Mario , Preusser Matthias , Roetzer-Pejrimovsky Thomas , Wöhrer Adelheid , Bogner Wolfgang , Rössler Karl , Trattnig Siegfried , Widhalm Georg 
Published: October 2024

AbstractObjectivesDiffuse gliomas, due to their infiltrative properties, still lack effective treatment options. Recent research indicates that infiltration, malignancy, and symptoms such as epilepsy are related to synaptic connections between infiltrating glioma cells and cytotoxic levels of glutamate release. We previously showed that high-resolution 7T magnetic resonance spectroscopic imaging (MRSI) can resolve metabolic heterogeneities in gliomas. With this study, we evaluated 7T MRSI-derived glutamate (Gln) and glutamine (Glu) ratio maps for their use in defining infiltrative tumor activity in the peritumoral region.Materials and MethodsWe analyzed 7T MRSI scans of 36 patients with low- and high-grade gliomas. Within the visible tumor and a peritumoral shell, we calculated medians and Dice similarity coefficients (DSC) for nine metabolic ratios with and without hotspot thresholding and evaluated their correlation to and statistical significance between clinical parameters (e.g., tumor-associated epilepsy, IDH status, grade).ResultsThe Glu/tCr (total creatine) median was significantly higher in the peritumoral VOI (1.13) compared to the tumor (0.92) and normal-appearing white matter (NAWM, 0.87), while the Gln/tCr median was highest in the tumor (0.77, vs 0.44 peritumoral and 0.33 in NAWM, all significantly different). Glu/tCho (total choline) was significantly higher in the peritumor as well (3.44 vs 2.23 tumoral and 2.06 in NAWM). Peritumoral DSCs for Glu/tCr and Gln/tCr hotspots were comparable (0.53 to 0.51). Peritumoral Gln/Glu was significantly different between patients with and without tumor-associated epilepsy, and intratumoral (Glu+Gln)/tCr was significantly different between IDH mutation and wildtype. IDH mutation correlated negatively with the intratumoral (Glu+Gln)/tCr median (-0.53) and high grade correlated with intratumoral Glx/tNAA, Glx/tCr, and Gln/tCr medians (0.50/0.53/0.58).Conclusions7T MRSI can not only map relevant metabolic information in the structurally visible tumor volume, but also detect infiltration in the peritumoral area. Gln and Glu are candidates for the development of presurgical imaging and treatment monitoring.
Prevalence and factors associated with iron deficiency anaemia among preterm infants attending clinics in Dodoma, Tanzania: A facility-based cross sectional study
Authors: Mchwampaka Christer R , Mahamba Dina C. , Matovelo Shubi , Jumanne Shakilu 
Published: October 2024

AbstractAimIron deficiency anemia (IDA) among preterm infants is a global public health problem due to its effects on development, immunity and general growth that may be irreversible. Despite the high burden of preterm births in Tanzania the prevalence and factors associated with IDA among preterm infants remains relatively understudied and this constituted of the aim of this study.MethodsA cross-sectional analytical study design was conducted among 190 preterm infants who attended pediatric clinic in Dodoma City from December 2022 to May 2023. Socio-demographic, clinical information, and laboratory markers of hemoglobin and iron status were obtained. Logistic regression analysis was applied to determine factors associated with the outcome (Iron Deficiency Anaemia).ResultsA total of 190 preterm infants were enrolled in the study and the mean age was 4.01 months (SD ±0.99 month. The prevalence of IDA among preterm infants was 11.58%. Factors associated with IDA were very low birth weight (AOR 6.906, CI: 1.4774-32.359, pvalue <0.0142), preterm infant not supplemented with Iron (AOR 6.282, CI:1.045-37.763, p value<0.0446, multiple pregnancies (AOR 6.848, CI:1.692-27.708,p value <0.0006) and severe anaemia during pregnancy (AOR 11.998, CI:5.068-40.197,p value<0.0001).ConclusionIron deficiency anemia was found to be 11.58% which fall under public health problem under WHO classification. To reduce IDA among preterm infants, there has to be an emphasis on iron supplementation to all preterm infants, and those with very low birth weight, born from mother who had multiple pregnancy and severe anaemia during pregnancy need close follow up and improved postnatalKeynotesThis study enrolled 190 preterm infants attending pediatric clinic in Dodoma city to determine factors associated with iron deficiency anaemia (IDA)Globally IDA in preterm infants range between 25 to 80, which complicates their prematurity by causing poor growth, poor functioning of multiple organ systems, poor neurological development and contributes to death and disabilityIron supplementation to all preterm infants is highly recommended
“Support for my dad would have benefited me because I was the one looking after him”: A qualitative analysis of the support needs of young people exposed to Adverse Childhood Experiences
Authors: Avery Flo , James Michaela , Cowley Laura Elizabeth , Griffiths Lucy , Bellis Mark A. , Hughes Karen , Brophy Sinead 
Published: October 2024

AbstractBackgroundAdverse childhood experiences (ACEs) are associated with negative health and wellbeing outcomes. Ensuring young people receive timely and appropriate support after experiencing ACEs could improve these outcomes.ObjectiveThis study aimed to explore what works to support young people living with ACEs; what support do they receive, and what are the characteristics of valuable help?Participants and SettingYoung people living in Wales aged 16–18 years (n=559) completed an online survey about their ACEs and the help they did or did not receive with these experiences.MethodsFree text responses were analysed using reflexive thematic analysis. Co-production workshops with young people were utilised to guide the analytic process.ResultsFew participants reported accessing enough support. Five themes were developed: “Help me by helping my family”, “Talking to a trusted adult is helpful… until it’s not”, “Being informed: ‘I was kept in the loop’”, “Schools and colleges as sites of support” and “Loneliness and peer support”.ConclusionsMore support is needed for young people with ACEs. Young people find it helpful when their whole family is supported in times of adversity, not blamed. People who provide support should be empathic and non-judgmental. Young people would rather be spoken to about ACEs and ‘kept in the loop’ than have them treated as a taboo or sensitive subject. Experiencing ACEs can be lonely in the absence of peer support. Schools and colleges are acceptable sites of support and may be well placed to provide opportunities for peer support.
PAV-spotter: using signal cross-correlations to identify Presence/Absence Variation in target capture data
Authors: de Visser Manon , Ploeg Chris van der , Cvijanović Milena , Vučić Tijana , Theodoropoulos Anagnostis , Wielstra Ben 
Published: October 2024

AbstractHigh throughput sequencing technologies have become essential in the fields of evolutionary biology and genomics. When dealing with non-model organisms or genomic gigantism, sequencing whole genomes is still relatively costly and therefore reduced-genome representations are frequently obtained, for instance by ‘target capture’ approaches. While computational tools exist that can handle target capture data and identify small-scale variants such as single nucleotide polymorphisms and micro-indels, options to identify large scale structural variants are limited. To meet this need, we introduce PAV-spotter: a tool that can identify presence/absence variation (PAV) in target capture data. PAV-spotter conducts a signal cross-correlation calculation, in which the distribution of read counts per target between samples of differenta prioridefined classes – e.g. male versus female, or diseased versus healthy – are compared. We apply and test our methodology by studyingTriturusnewts: salamanders with gigantic genomes that currently lack an annotated reference genome.Triturusnewts suffer from a hereditary disease that kills half their offspring during embryogenesis. We compare the target capture data of two different types of diseased embryos, characterized by unique deletions, with those of healthy embryos. Our findings show that PAV-spotter helps to expose such structural variants, even in the face of medium to low sequencing coverage levels, low sample sizes, and background noise due to mis-mapped reads. PAV-spotter can be used to study the structural variation underlying supergene systems in the absence of whole genome assemblies. The code, including further explanation, is available through the PAV-spotter GitHub repository:https://github.com/Wielstra-Lab/PAVspotter.
Assessing the Incidence and Severity of Field Diseases in Marigold (Tagetes erecta) Cultivation: Results from Jashore District, Bangladesh
Authors: Shome Anannya , Ahmmed Abu Noman Faruq , Zaman Rabita 
Published: October 2024

AbstractA research investigation was carried out in the Jashore district of Bangladesh to study diseases affecting marigold plants, while the laboratory analyses were conducted in the Plant Disease Study Facility, Sher-e-Bangla Agricultural University, Dhaka. The experiment was conducted by surveying three farmers from each of the sixteen villages across four unions in Jhikargacha Upazila to document the occurrence and severity of diseases under natural field conditions. The fungal pathogens surveyed in these diseases were six: leaf spot, botrytis blight, flower bud rot, stem rot, foliage blight, white mold-soil-borne diseases. The most important microflora reported to be associated with these diseases includeAlternaria alternata, Botrytis cinerea, Alternaria dianthi, Fusarium oxysporum, Curvularia lunata, andSclerotinia sclerotiorum. Of these, major diseases included leaf spot, botrytis blight, foliage blight or flower and leaf blight, and flower bud rot, while minor diseases included stem rot and white mold. Disease incidence ranged from 0-54.33%, while disease severity varied from 1-24%. The paper is the first record of marigold pathogens in Bangladesh and will be highly useful during disease identification, and it would lead to the better cultivation of marigolds in this area.