bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Inspiratory Muscle Training in Patients with COPD Exacerbations: A Randomized Controlled Trial
Authors: Huang Wei-Yu , Lee Chien-Tzu , Chien Jung-Yien , Hsu Miaoju , Wang Li-Ying
Published: November 2024

Objective

Pulmonary rehabilitation (PR) is considered for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, the impact of adding inspiratory muscle training (IMT) to PR on inspiratory muscle function is underexplored. This study aimed to evaluate the effects of IMT in addition to PR on inspiratory muscle function, functional exercise capacity (FEC), and quality of life (QoL) in patients with AECOPD.

Design

16 patients with AECOPD and a maximal inspiratory pressure (PImax) < 80 cmH2O were randomized into the experimental (PR + IMT) or the control (PR + sham IMT) group for an eight-week intervention. Inspiratory muscle activation was measured using surface electromyography, FEC was examined by six-minute walk distance (6MWD), and QoL was assessed with COPD Assessment Test (CAT).

Results

The experimental group showed sustained and significant improvements in inspiratory muscle function, 6MWD, and QoL after intervention (all p < 0.05). The experimental group had higher PImax with less diaphragm activation (both p < 0.001) and more improvements in 6MWD and QoL after intervention (both p < 0.05).

Conclusions

Adding IMT to PR resulted in more improvements in inspiratory muscle function, FEC, and QoL for patients with AECOPD, suggesting IMT as a beneficial addition to PR.
Associations Between Partial Common Extensor Tendon Tears and Clinical-Ultrasonographic Findings in Patients with Lateral Epicondylitis
Authors: Songur Kadir , Gunduz Nihan Erdinc , Ellidokuz Hulya , Gulbahar Selmin , Dilek Banu
Published: November 2024

Objective

This study investigates the effect of partial common extensor tendon(CET) tears on demographic, clinical and ultrasound(US) parameters in patients with lateral epicondylitis(LE). The research aimed to provide a comprehensive understanding of how these US-detected partial tears, influence the clinical presentation and imaging findings associated with LE.

Design

Retrospective cross-sectional study. Records of 227 patients with LE met the inclusion-exclusion criteria were reviewed. The patient-rated tennis elbow evaluation(PRTEE) questionnaire was used as the primary outcome measure. Secondary outcome measures were visual analogue scale for pain, algometric measurements, hand grip strength, and US parameters(maximum tendon thickness measurements from the capitellar-radiocapitellar sides and presence of bone abnormalities).

Results

Partial CET tears detected using US were present in 22.7%(n = 54) of the patients. Patients with partial CET tears were older(50,31±9,22), had higher PRTEE scores(65,48±12,76), capitellar-radiocapitellar maximum tendon thickness measurements(0,60±0,80/0,60±0,72), and incidence of bone abnormalities(%40.7,n = 32)(p < .05). Logistic regression analysis identified age and PRTEE pain score as associative factors with partial CET tears. Capitellar-radiocapitellar maximum tendon thickness measurements greater than 0.55 cm were associated with an increased probability of partial CET tears.

Conclusion

US-detected partial CET tears may worsen the clinical and US parameters in patients with LE. Older age, higher PRTEE scores and higher maximum tendon thickness measurements were associated with partial CET tears.
Effects of Exercise on Pain and Disability after Lumbar Disc Herniation Surgery: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Jin Hyungsub , Kwon JangWon , Lee Jeongmin , Lee Dong Joon , Lee Dong Hoon , Jeon Justin Y.
Published: November 2024

Objective

To evaluate the effects of postoperative exercise on pain, disability, and quality of life (QoL) after lumbar disc herniation (LDH) surgery.

Design

We systematically searched the PubMed, EMBASE, Cochrane Library, and PEDro databases up to May 2024. Two reviewers independently selected and assessed relevant randomized controlled trials investigating the effectiveness of postoperative exercise after the surgical treatment of patients diagnosed with LDH on low back pain, disability, and QoL assessed using visual analog scale (VAS), Oswestry Disability Index (ODI), and 36-item short-form health survey (SF-36).

Results

Compared with the control group, the exercise intervention group had significantly lower VAS scores both in the short-term (effect: 95% confidence interval [CI] = −1.14 [−1.69, −0.58], I2 = 91.4%, n = 9 studies) and long-term follow-up (effect: 95% CI = −0.99 [−1.89, −0.08], I2 = 58.9%, n = 4 studies). The exercise intervention group also showed a significant reduction in ODI in the short-term follow-up (effect: 95% CI = −5.00 [−7.72, −2.29], I2 = 79.3, n = 7 studies), but not in the long-term follow-up (effect: 95% CI = −0.80 [−3.97, 2.38], I2 = 0.0%, n = 4 studies). Among the SF-36 factors, physical function, pain, general health, energy and vitality, and social function significantly improved in the exercise intervention group compared with the control group.

Conclusions

Exercise after LDH surgery was effective in reducing pain and disability and improving QoL.
Determination of the Surgical Landmarks for the Anterior and Middle Cranial Fossa in Dry Skulls With the Photography System, Cadavers and 3-Dimensional Computed Tomography
Authors: Polat Sema , Tunç Mahmut , Öksüzler Fatma Yasemin , Öksüzler Mahmut , Özşahin Esin , Göker Pinar
Published: November 2024

The present paper was designed to analyze the dimensions of such important bony structures and surgical landmarks, which are used by many clinicians in many surgical interventions, in dry skull, cadaver, and healthy subjects on computed tomography (CT) images, and to determine whether there is a significant difference between these methods, and to obtain reference values from 3 different methods. Eight cadavers and 16 dry skulls and 100 three-dimensional (3D) CT images were studied. Necessary permissions for the study were obtained from Ethics Comittee. The 16 parameters were measured with an electronic digital caliper accurate 0.01 mm (LCD Digital Vernier Dial Microcaliper (INCA, DCLA-0605, 0.6–150 mm, USA). Also, the images obtained were transferred to the 3D Slicer (version 5.6.2) software program. Eight cadavers and 16 dry skulls of Turkish adults were unknown age and sex, whereas the mean age of females and males on CT images were 31.63±11.23 and 33.70±13.34 years, respectively. All values of the surgical landmarks for the anterior and middle cranial fossa obtained from cadavers, dry skulls, and 3D CT subjects (except length of lesser wing, anterior clinoid lengths for 2 sides, and width for right side) were statistically significant between 3 groups (P<0.05). This paper was conducted for the morphometric analysis of the specific regions of the anterior cranial fossa (ACF), and middle cranial fossa (MCF), which are used in neurosurgical procedures This detailed anatomic and radiologic reference values will be an extremely important source in the planning of both clinical and surgical approaches for neurosurgeon, anatomist, and radiologists.
Multi-Metal Resistant Staphylococcus warneri strain TWSL_1: Revealing Heavy Metal Resistant Genomic Features by Whole Genome Sequencing
Authors: Dissanayake Dilani , Chandrasekharan2 Naduviladath , Wijayaratahna Dilrukshi
Published: November 2024

2. Abstract This study investigates the genomic basis of heavy metal resistance in Staphylococcus warneri strain TWSL_1, isolated from the effluent of a textile dyeing industry in Sri Lanka. The strain exhibited substantial resistance to Cd2+, Pb2+, and Cu2+, with minimum inhibitory concentrations of 50 mg/L, 1200 mg/L, and 75 mg/L, respectively. Phylogenetic analysis of the 16S rRNA gene confirmed its placement within the Staphylococcus warneri clade. Whole genome sequencing revealed a 2,661,318 bp genome with 2,567 coding sequences and a 99.81% average nucleotide identity with S. warneri WS479. Key heavy metal resistance genes were identified through the comparative genomic analysis, including those encoding a cadmium efflux system accessory protein and a cadmium resistance protein uniquely present in TWSL_1 but absent in Staphylococcus aureus RF122. The strain also harbored genes associated with cobalt-zinc-cadmium resistance underscoring TWSL_1's adaptation to heavy metal stress. These findings highlight the comprehensive genetic toolkit of TWSL_1, enhancing its resilience in metal-contaminated environments and demonstrating its potential for bioremediation, supporting the development of eco-friendly remediation strategies.
Effect of laboratory manual layout: Does experiential learning benefits from authentic context?
Authors: Easton Victoria Alice Kate
Published: November 2024

2. Abstract

Experiential learning is the pedagogic foundation of practical laboratory education. This process of learning though experience enables students to develop a deeper understanding of the theoretical material as well as valuable real-world skills. However, there is often a disconnect between the authentic, real-world context of performing laboratory skills; and the method of instruction within higher education. This study developed two student laboratory manuals; one which followed a traditionally linear “week by week” format, and another which took inspiration from a publication format and listed the protocols in a distinct “methods” section. The effect the change of layout had on student learning was assessed through analysis of student summative assessment and interaction with the online learning environment. Additionally, the effect student confidence and perceived technical skills development was assessed through a student survey. The differences in layout resulted in no significant differences in student assessment performance but did result in higher levels of engagement with the online learning environment. The student survey reported an increase in technical confidence (21%) and skill (31%) with the authentic “methods” section layout changes compared to the traditional format. The increase in student engagement, confidence, and perceived skill shows that experiential learning does benefit from placing the information in an authentic context.
Exploring the associations between screen time and sleep quality in adult residents of Ahmedabad: A cross-sectional study
Authors: Patel Bharat , Pandya Disha , Prajapati Bhadrawati , Doshi Omkumar
Published: November 2024

Background and rationale

Sleep is essential for overall health, but many people experience disruptions in their sleep patterns due to busy schedules and technology use. This study aims to examine the impact of screen time on sleep quality, considering factors such as smoking, caffeine use, napping, physical activity, and stress levels.

Methodology

We enrolled 371 participants from Ahmedabad for a cross-sectional study. 278 participants were included after the screening. They completed a questionnaire covering demographics, screen time, covariates, stress using the PSS10 scale, and sleep quality using the PSQI scale. Data was analyzed using IBM SPSS Statistics 22 after entering it into MS Excel.

Result

This study examines the relationships between lifestyle factors and sleep quality among adults aged 18 to 40 in Ahmedabad. The findings suggest that increased screen time (r = 0.518, p < 0.001) and higher stress levels (r = 0.530, p < 0.001) are associated with lower sleep quality. Additionally, nap frequency and physical activity show significant associations with sleep ratings. However, no significant connections were found between caffeine use at night or smoking/tobacco usage and sleep quality.

Conclusion

The Spearman correlation indicates a moderate correlation between screen time and sleep quality, as well as between stress and sleep quality. The chi-square test shows significant associations between screen time before bed, napping, and physical activity with sleep quality. However, there is no association between nighttime caffeine consumption or smoking/tobacco use and sleep quality.
HuBERT-ECG: a self-supervised foundation model for broad and scalable cardiac applications
Authors: Coppola Edoardo , Savardi Mattia , Massussi Mauro , Adamo Marianna , Metra Marco , Signoroni Alberto
Published: November 2024

Deep learning models have shown remarkable performance in electrocardiogram (ECG) analysis, but their success has been constrained by the limited availability and size of ECG datasets, resulting in systems that are more task specialists than versatile generalists. In this work, we introduce HuBERT-ECG, a foundation ECG model pre-trained in a self-supervised manner on a large and diverse dataset of 9.1 million 12-lead ECGs encompassing 164 cardiovascular conditions. By simply adding an output layer, HuBERT-ECG can be fine-tuned for a wide array of downstream tasks, from diagnosing diseases to predicting future cardiovascular events. Across diverse real-world scenarios, HuBERT-ECG achieves AUROCs from 84.3% in low-data settings to 99% in large-scale setups. When trained to detect 164 overlapping conditions simultaneously, our model delivers AUROCs above 90% and 95% for 140 and 94 diseases, respectively. HuBERT-ECG also predicts death events within a 2-year follow-up with an AUROC of 93.4%. We release models and code.
Age-Normalized Testosterone Peaks at Series B for Male Startup Founders
Authors: Moradian Jordan , Dubrovsky Michael , Sama Megha , Korecky Pavel , Kulkarni Sidarth , Goder Yaniv , Vermeulen Diedrik
Published: November 2024

In a study of 107 male Y Combinator founders, a surprising correlation between age-normalized testosterone and company stage was uncovered. Testosterone, a hormone associated with confidence, dominance, and drive, increased by 55.7% from pre-seed to seed funding, peaking at the Series B stage, where levels were 99.6% higher than pre-seed. After series B funding, testosterone was observed to drop by 42.2%, coinciding with a spike in cortisol levels. This age-normalized biomarker analysis supports the dual-hormone hypothesis, illustrating that early startup success fosters feelings of dominance and confidence, while later-stage pressures and stresses erode these feelings. An alternative interpretation of the data, which suggests the opportunity for a longitudinal study, is that male founders with higher testosterone are more likely to raise larger rounds of funding.
Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches
Authors: Chen Xiongfeng , Zhou Zhuan , Yazgan Zeynep , Xie Luyu , Rossi Francesca , Liu Yang , Zhang Bo , Polanco Patricio M. , Zeh Herbert J. , Kim Alex C. , Huang Huocong
Published: November 2024

Recent studies have identified a unique subtype of cancer-associated fibroblasts (CAFs) termed antigen-presenting CAFs (apCAFs), which remain the least understood CAF subtype. To gain a comprehensive understanding of the origin and function apCAFs, we construct a fibroblast molecular atlas across 14 types of solid tumors. Our integration study unexpectedly reveals two distinct apCAF lineages present in most cancer types: one associated with mesothelial-like cells and the other with fibrocytes. Using a high-resolution single-cell spatial imaging platform, we characterize the spatial niches of these apCAF lineages. We find that mesothelial-like apCAFs are located near cancer cells, while fibrocyte-like apCAFs are associated with tertiary lymphoid structures. Additionally, we discover that both apCAF lineages can up-regulate the secreted protein SPP1, which facilitates primary tumor formation and peritoneal metastasis. Taken together, this study offers an unprecedented resolution in analyzing apCAF lineages and their spatial niches.
AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers
Authors: Weichert Fabian G. , Inostroza Pedro A. , Ahlheim Jörg , Backhaus Thomas , Brack Werner , Brauns Mario , Fink Patrick , Krauss Martin , Svedberg Patrik , Hollert Henner
Published: November 2024

The vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need to be modified ensuring that they remain aligned with the rapid development and marketing of new substances. Here we performed a longitudinal chronic mixture risk assessment considering a real-world case study scenario with diverse anthropogenic impact types characterised by different land uses along a river in Central Germany. We sampled river water using large-volume solid phase extraction at six selected sampling sites. Following chemical analysis using liquid chromatography-high resolution mass spectrometry, we quantified 192 substances. For 34% of them, we obtained empirical chronic effect data for freshwater organisms. Furthermore, we used the open-source artificial intelligence (AI) model TRIDENT to predict chronic toxicity for all substances. A multi-scenario mixture risk assessment was conducted for three taxonomic groups, using the concentration-addition concept and considering various hazard and exposure scenarios. The results showed that the chronic risk estimates for all taxonomic groups were considerably higher when the empirical data was amended with data from in silico modelling. We identified hot spots of chemical pollution and our analysis indicated that fish were the most vulnerable taxonomic group, with pharmaceuticals being the most relevant risk drivers. Our study exemplifies the application of an AI model to predict chronic risk for aquatic organisms in combination with the consideration of multiple risk scenarios, that may complement future risk assessment strategies.

Highlights

192 organic chemicals were quantified in six surface water samples along a river.

Multiple hazard and exposure scenarios were considered in mixture risk assessment.

Artificial intelligence was used to fill data gaps and predict chronic ecotoxicity.

Fish were identified as the most vulnerable taxonomic group for chronic toxicity.

Pharmaceuticals were the most prevalent mixture risk drivers.

Graphical abstract
Global risk mapping of highly pathogenic avian influenza H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onward
Authors: Dupas Marie-Cécile , Vincenti-Gonzalez Maria F. , Dhingra Madhur , Guinat Claire , Vergne Timothée , Wint William , Hendrickx Guy , Marsboom Cedric , Gilbert Marius , Dellicour Simon
Published: November 2024

Avian influenza (AI) is a highly contagious viral disease affecting poultry and wild water birds, posing significant global challenges due to its high mortality rates and economic impacts. Highly pathogenic avian influenza (HPAI) outbreaks, particularly those caused by H5N1 and its variants, have surged since their first occurrence in 1959. The HPAI H5N1 clade 2.3.4.4b viruses have notably expanded its geographical reach, affecting numerous countries, diverse avian species, and now wild and domestic mammals. Using an ecological niche modelling approach, this study aims to elucidate the environmental factors associated with the increased HPAI H5 cases since 2020, investigate potential shifts in ecological niches, and predict new areas suitable for local viral circulation. Focusing on H5N1 and H5Nx strains, we have developed ecological niche models for HPAI case in both wild and domestic birds while considering two distinct periods: 2015-2020 and 2020-2022. Key environmental predictors include chicken and duck population density, human density, distance to water bodies, and several land cover variables. Post-2020, we observe a notable increase in the relative importance of some of these predictors, such as intensive chicken population density and cultivated vegetation. The resulting risk maps reveal notable ecological suitability for local HPAI H5 circulation in Europe, Asia, as well as North and South America, with notable expansions of the areas at risk post-2020. The spatial distribution of HPAI H5 occurrences in wild birds appears to be primarily influenced by urban areas and open water regions. Overall, global risk maps derived from our models identify regions at risk where surveillance and control measures should be prioritised. Finally, our analyses also highlight a shift in the diversity of species affected by HPAI outbreaks, with a higher variety of avian species, particularly sea birds, being impacted post-2020. This increased diversity suggests that ecological shifts in HPAI H5 circulation may be accompanied by a broader range of susceptible species. Overall, these results further contribute to the understanding of HPAI epidemiology.
Automatic Discovery of Optimal Discrete Character Models
Authors: Boyko James D.
Published: November 2024

Modeling discrete character evolution in a Markovian framework has become the standard in phylogenetic comparative methods. The increasing size and complexity of these models reflects a trend of analyses to include more taxa and more discrete characters. However, as complexity of the models increase, so do the number of potential model structures and number of estimable parameters, making it nearly impossible to consider all modeling options for a given dataset. To overcome this issue, I apply a combination of regularization and parameter sharing optimization to models of discrete character evolution. This allows for the automatic searching and optimization across different model structures without user specification. I test this framework under several simulation scenarios including hidden rates and multiple discrete characters. The results indicate that regularized models significantly outperform traditional approaches, yielding a far lower variance and a nearly tenfold reduction in the overall error of parameter estimates in the most extreme scenarios. I illustrate the power of automatic model selection by revisiting the ancestral state estimation of concealed ovulation and mating systems in Old-World monkeys. Using the dredge algorithm, I discover a previously unexamined model structure which has both better statistical performance and a differing ancestral state reconstruction when compared to default model sets. In general, these results highlight the dangers of an over-reliance on default model sets. The combination of automatic model selection and regularization help overcome problems of over-parameterization, and these results demonstrate that when inferences are drawn from a larger model space, they are both more statistically robust and biologically realistic.
Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation
Authors: Parkinson J E , Ghafoor M , Dodd R J , Tompkins H E , Fergie M , Rattray M , Allen J E , Sutherland T E
Published: November 2024

The extracellular matrix (ECM) forms the scaffold in which cells reside and interact. The composition of this scaffold guides the development of local immune responses and tissue function. With the advent of multiplexed spatial imaging methodologies, investigating the intricacies of cellular spatial organisation are more accessible than ever. However, the relationship between cellular organisation and ECM composition has been broadly overlooked. Using imaging mass cytometry, we investigated the association between cellular niches and their surrounding matrix environment during allergic airway inflammation in two commonly used mouse strains. By first classifying cells according to their canonical intracellular markers and then by developing a novel analysis pipeline to independently characterise a cells ECM environment, we integrated analysis of both intracellular and extracellular data. Applying this methodology to three distinct tissue regions we reveal disparate and restricted responses. Recruited neutrophils were dispersed within the alveolar parenchyma, alongside a loss of alveolar type I cells and an expansion of alveolar type II cells. This activated parenchyma was associated with increased proximity to hyaluronan and chondroitin sulphate. In contrast, infiltrating CD11b+and MHCII+cells accumulated in the adventitial cuff and aligned with an expansion of the subepithelial layer. This expanded subepithelial region was enriched for closely interacting stromal and CD11b+immune cells which overlaid regions enriched for type-I and type-III collagen. The cell-cell and cell-matrix interactions identified here will provide a greater understanding of the mechanisms and regulation of allergic disease progression across different inbred mouse strains and provide specific pathways to target aspects of remodelling during allergic pathology.

Graphical Abstract
HSP90 buffers deleterious genetic variations inBRCA1
Authors: Gracia Brant , Montes Patricia , Huang Min , Chen Junjie , Karras Georgios Ioannis
Published: November 2024

Protein-folding chaperone HSP90 buffers genetic variation in diverse organisms, but the clinical significance of HSP90 buffering in disease remains unclear. Here, we show that HSP90 buffers mutations in the BRCT domain of BRCA1. HSP90-bufferedBRCA1mutations encode protein variants that retain interactions with partner proteins and rely on HSP90 for protein stability and function in cell survival. Moreover, HSP90-buffered BRCA1 variants confer PARP inhibitor resistance in cancer cell lines. Low-level HSP90 inhibition alleviates this resistance, revealing a cryptic and mutant-specific HSP90-contingent synthetic lethality. Hence, by stabilizing metastable variants across the entirety of the BRCT domain, HSP90 reduces the clinical severity of BRCA1 mutations allowing them to accumulate in populations. We estimate that HSP90 buffers 11% to 28% of known human BRCA1- BRCT missense mutations. Our work extends the clinical significance of HSP90 buffering to a prevalent class of variations inBRCA1, pioneering its importance in cancer predisposition and therapy resistance.
OncotreeVIS – an interactive graphical user interface for visualizing mutation tree cohorts
Authors: Baciu-Drăgan Monica-Andreea , Beerenwinkel Niko
Published: November 2024

Motivation

In recent years, developments in single-cell next-generation sequencing technology and computational methodology have made it possible to reconstruct, with increasing precision, the evolutionary history of tumors and their cell phylogenies, represented as mutation trees. Many mutation tree inference tools exist, but they do not support detailed visual tree inspection, nor tree comparisons or analysis at the cohort level, an important task in computational oncology.

Results

We developed oncotreeVIS, an interactive graphical user interface for visualizing mutation tree cohorts and tree posterior distributions obtained from mutation tree inference tools. Oncotree-VIS can display mutation trees that encode single or joint genetic events, such as point mutations and copy number changes, and highlight matching subclones, conserved trajectories and drug-gene interactions at cohort level. OncotreeVIS facilitates the visual inspection of mutation tree clusters and pairwise tree distances. It is available both as a JavaScript library that can be used locally or as a web application that can be accessed online. It includes seven default datasets of public mutation tree cohorts for visualization, while new mutation trees are provided in a predefined JSON format.

Availability and implementation

https://github.com/cbg-ethz/oncotreeVIS.

Web application

https://cbg-ethz.github.io/oncotreeVIS.
Giving it a fair shake: A simple, fast, & efficient method to extract amatoxins from the death cap mushroom,Amanita phalloides
Authors: Adams Catharine A. , Bever Candace S. , Blake-Hedges Jacquelyn M. , Brown Sean , Thompson Mitchell G. , Behie Scott , Keasling Jay , Shih Patrick
Published: November 2024

1.1.

The death cap mushroom,Amanita phalloides, is well known for containing amatoxins such as alpha- and beta-amanitin, which inhibit eukaryotic RNA polymerase II. While these toxins have been used in research for almost a century, they have recently garnered attention for their role in drug-antibody conjugates. The amatoxins are still largely extracted from wild mushrooms, which cannot be made to fruit in the lab.

We propose simplified extraction methods that could reduce hazardous exposures to dust and expedite sample analysis without sacrificing accuracy. We recently developed a Lateral Flow Immunoassay (LFIA), for which we identified that sample maceration was not needed to extract the amatoxins and that the incubation time for extraction could be accomplished in 1 minute. In this current work, we hypothesized that these same extraction adjustments–minimal tissue maceration and reduced incubation time–could be transferable to instrumental detection methods.

To test the need for sample maceration, we utilized three different techniques: 1) traditional mortar and pestle, 2) a similarly disruptive method of bead beating, and 3) no grinding, but rather hand shaking dried mushroom tissue in extraction buffer. In addition, we performed the solvent extraction step at varying times to observe if more time allows for more toxin to be removed from the tissue. Lastly, we utilized two comparable solvent evaporation methods (rotovap or speedvac) to establish if multiple samples could be processed simultaneously, thus improving sample throughput.

We adjusted aspects of the typical extraction protocol, which resulted in a rapid (1 min) incubation step, along with minimal sample handling (no grinding) of the dried mushroom tissue. We present an extraction protocol that saves time, reduces equipment contamination, and minimizes risk to the researcher. The impact of this faster, safer method may help produce these important toxins faster, for both research and medical use.
Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding
Authors: Singh Sukrit , Gapsys Vytautas , Aldeghi Matteo , Schaller David , Rangwala Aziz M , White Jessica B , Bluck Joseph P , Scheen Jenke , Glass William G , Guo Jiaye , Hayat Sikander , de Groot Bert L , Volkamer Andrea , Christ Clara D , Seeliger Markus A , Chodera John D
Published: November 2024

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We find that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.
The follicular lymphoma and chronic lymphocytic leukemia proliferative microenvironment at single-cell resolution
Authors: Ferreira Antonio P. , Wang Shumei , Poluben Laryssa , Brandstadter Joshua , Perkey Eric , Sotirakos Steven , Drew Mai , Pan Li , Lemieux Madeleine E. , Dorfman David M. , Shoji Brent , Maillard Ivan , Blacklow Stephen C. , Aster Jon C.
Published: November 2024

Lymph nodes (LNs) are secondary lymphoid organs where lymphocytes interact with antigen presenting cells to initiate adaptive immune responses within microenvironments established by resident stromal cells. LNs are also the major site of growth of follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), B cell neoplasms that alter the stromal architecture of LNs in highly stereotypic ways. To characterize FL and CLL cells within the LN microenvironment, we developed a pipeline for single-cell RNA sequencing of all resident LN cells. We observed that proliferation of FL and CLL cells within specialized niches commences with transient upregulation ofMYC,subsequent downregulation of which may act to limit the growth potential of these indolent neoplasms. Proliferating FL cells within neoplastic follicles co-localized with follicular dendritic cells, whereas proliferating CLL cells were spatially associated with a distinct set of fibroblasts expressing CCL19 that localized to proliferation centers. We used informatic analyses and microscopy to identify and validate interacting sets of ligand-receptor pairs between proliferating neoplastic B cells, immune cells and stromal fibroblasts, including interactions involving CD74-MIF, TNFRSF13C (BAFF receptor), immunomodulatory factors such as CD55 and Galectin-9 (Gal9), and adhesion molecules. Our analyses highlight common features of these two microenvironment-dependent neoplasms and provide a roadmap for identifying vulnerabilities and new therapeutic strategies.

Key points

Nodal FL and CLL cells have similar proliferative programs and predicted ligand-receptor interactions with immune and stromal cells.

Proliferation of FL and CLL cells occurs in distinct lymph node niches defined by different types of fibroblasts.
Rhinovirus as a Driver of Airway T-Cell Dynamics in Children with Severe Asthma
Authors: Bryant Naomi , Muehling Lyndsey M. , Wavell Kristin , Teague W. Gerald , Woodfolk Judith A.
Published: November 2024

Severe asthma in children is notoriously difficult to treat, and its immunopathogenesis is complex. In particular, the contribution of T cells and relationships to anti-viral immunity, remain enigmatic. Here, we coupled deep phenotyping with machine learning methods to resolve the dynamics of T cells in the diseased lower airways, and examined rhinovirus (RV) as a driver. Our strategy revealed a T-cell landscape dominated by type 1 and type 17 CD8+ signatures. Interrogation of phenotypic relationships coupled with trajectory mapping identified T-cell migratory and differentiation pathways spanning the blood and airways that culminated in tissue residency, and included transitions between type 1 and type 17 tissue-resident types. These T-cell dynamics were reflected in cytokine polyfunctionalityin situ. Use of machine learning to cross-compare T-cell populations that were enriched in the airways of RV-positive children with those induced in the blood after RV challenge in an experimental infection model, precisely pinpointed RV-responsive signatures that mapped to T-cell differentiation pathways. Despite their rarity, these signatures were detected in the airways of uninfected children. Together, our results underscore the aberrant nature of type 1 immunity in the airways of children with severe asthma, and implicate an important viral trigger as a driver.
Cross-tissue Graph Attention Networks for Semi-supervised Gene Expression Prediction
Authors: Wang Shiyu , He Mengyu , Qin Muran , Hu Yijuan , Zhao Liang , Qin Zhaohui
Published: November 2024

High-throughput biotechnologies have significantly advanced precision medicine by enabling the exploitation of global gene expression patterns to enhance our understanding of disease etiology, progression, and treatment options. However, the tissue-specific nature of gene expression presents a challenge, particularly for less accessible tissues such as the brain, underscoring the need for computational methods to accurately impute gene expression in these critical but hard-to-reach tissues. While several attempts to impute gene expression in tissue-specific contexts have shown promising results, their reliance on regression analysis faces limitations due to the inability to capture complex, nonlinear relationships in gene expression patterns. In contrast, modern machine learning techniques, particularly graph neural networks, have demonstrated superior performance by efficiently modeling the intricate interactions among genes across different tissues. Therefore, we introduce gene expression imputation with Graph Attention Networks (gemGAT), a novel approach leveraging Graph Attention Networks (GATs) to enhance gene expression prediction across different tissues. gemGAT distinguishes itself by predicting the expression of all genes simultaneously, utilizing the full spectrum of genomic data to account for gene co-expressions and non-linear relationships. Validated through extensive experiments with Genotype-Tissue Expression (GTEx) data and a case study from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), gemGAT demonstrates superior performance over existing methods by efficiently capturing non-linear gene co-expressions. This advancement underscores gemGAT’s potential to significantly contribute to precision medicine, showcasing its utility in advancing our understanding of gene expression in less accessible tissues.
Efficacy and Safety of Total Body Irradiation versus Chemotherapy Conditioning for Hematopoietic Stem Cell Transplant in Adult Acute Lymphoblastic Leukemia: A Systematic Review and Meta-Analysis
Authors: Ans Hasaan , Amjad Haiqah , Nazir Sara , Cheema Sara Sajjad , Mistry Dhruv , Fatima Tazeen , Khan Ahmed Yar , Imtiaz Muhammad Usama , Khan Fasih Mand , Khalid Saif , Rehman Mohammad Ebad Ur , Faisal Muhammad Salman
Published: November 2024

Background

Hematopoietic stem cell transplantation (HSCT) is a potentially curative option for adults with acute lymphoblastic leukemia (ALL) who have achieved remission. This systematic review and meta-analysis compare the efficacy of total body irradiation (TBI) versus chemotherapy (CHT) based regimens for conditioning in adult ALL patients being prepared for HSCT.

Methods

A comprehensive literature search was conducted in MEDLINE, Embase, the Cochrane Library, and relevant trial registries from their inception to August 2024. The inclusion criteria encompassed all randomized controlled trials (RCTs) and cohort studies that compared TBI) with CHT as conditioning regimens prior to HSCT in adult patients with ALL. The primary outcomes assessed were overall survival (OS) and event-free survival (EFS). All statistical analyses were carried out using RevMan version 5.4, using a random-effects model.

Results

This meta-analysis includes 20 cohort studies and one RCT. The relative risk (RR) for OS was 1.37 (95% CI: 1.20–1.57), while the RR for EFS was 1.28 (95% CI: 1.15–1.43), highlighting the superior efficacy of TBI-based regimens in this patient population. TBI was also superior in terms of relapse rate (RR 0.71). The two regimens were comparable in terms of non-relapse mortality, acute graft-versus-host disease (GVHD), and chronic GVHD.

Conclusion

When used for conditioning prior to HSCT, TBI-based conditioning regimens demonstrate superior OS, EFS, and relapse outcomes compared to CHT-based regimens.
Modeling dynamic disease-behavior feedbacks for improved epidemic prediction and response
Authors: Du Hongru , Zahn Matthew , Loo Sara , Alleman Tijs , Truelove Shaun , Patenaude Bryan , Gardner Lauren , Papageorge Nicholas , Hill Alison
Published: November 2024

Human behavior significantly influences infectious disease transmission, yet traditional models often overlook this factor, limiting predictions of disease and the associated socioeconomic impacts. We introduce a feedback-informed epidemiological model that integrates economic decision-making with infectious disease dynamics. Individuals weigh costs and benefits, then choose behaviors that influence their risk of infection and disease progression, thereby shaping population-level dynamics. Applying this model to a scenario based on the early COVID-19 pandemic, we examine decisions to abstain from work to mitigate infection risk. Our findings reveal that feedback between disease and behavior notably affect infection rates and overall welfare, especially when accounting for individual economic and health vulnerabilities, which are often in tension. We evaluate counterfactual policies, including labor restrictions and cash transfers, illustrating how our framework can simultaneously address epidemiological, economic, and equity-related questions. This flexible and extendable modeling framework offers a powerful tool for assessing infectious disease interventions.
Comparative Performance of Claude and GPT Models in Basic Radiological Imaging Tasks
Authors: Nguyen Cindy , Carrion D , Badawy MK
Published: November 2024

Background

Publicly available artificial intelligence (AI) Visual Language Models (VLMs) are constantly improving. The advent of vision capabilities on these models could enhance workflows in radiology. Evaluating their performance in radiological image interpretation is vital to their potential integration into practice.

Aim

This study aims to evaluate the proficiency and consistency of the publicly available VLMs, Claude and GPT, across multiple iterations in basic image interpretation tasks.

Method

Subsets from publicly available datasets, ROCOv2 and MURAv1.1, were used to evaluate 6 VLMs. A system prompt and image were inputted into each model thrice. The outputs were compared to the dataset captions to evaluate each model’s accuracy in recognising the modality, anatomy, and detecting fractures on radiographs. The consistency of the output across iterations was also analysed.

Results

Evaluation of the ROCOv2 dataset showed high accuracy in modality recognition, with some models achieving 100%. Anatomical recognition ranged between 61% and 85% accuracy across all models tested. On the MURAv1.1 dataset, Claude-3.5-Sonnet had the highest anatomical recognition with 57% accuracy, while GPT-4o had the best fracture detection with 62% accuracy. Claude-3.5-Sonnet was the most consistent model, with 83% and 92% consistency in anatomy and fracture detection, respectively.

Conclusion

Given Claude and GPT’s current accuracy and reliability, integration of these models into clinical settings is not yet feasible. This study highlights the need for ongoing development and establishment of standardised testing techniques to ensure these models achieve reliable performance.
The lived experience of rejection sensitivity in ADHD - a qualitative exploration
Authors: Rowney-Smith Annabel , Sutton Beth , Quadt Lisa , Eccles Jessica A
Published: November 2024

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition, commonly associated with differences in attention, and/or hyperactivity and impulsiveness. A lesser known, but highly impactful characteristic of ADHD is emotional dysregulation, which causes difficulties in emotional expression and identification. An aspect of emotional dysregulation which remains relatively unexplored in ADHD, is rejection sensitivity. In people who experience rejection sensitivity, perceived rejection and/or criticism can evoke extreme dysphoria. To investigate the lived experience of rejection sensitivity in ADHD individuals, five undergraduates participated in focus-group interviews. The subsequent thematic analysis revealed three key themes which encapsulated the experience;withdrawal,masking, andbodily sensations. The participants explained how rejection sensitivity elicited unpleasant bodily sensations, anxiety, and misery, and how, in turn, they used masking to camouflage these feelings. They then discussed how the use of masking had caused them to become dissociated from themselves and to withdraw from others, which often resulted in loneliness. It was apparent that rejection sensitivity significantly impacts mental wellbeing, eliciting feelings such as anxiousness, despair, and embarrassment. In turn, social function is significantly impaired, alongside career opportunities, and daily life. Our findings indicate a need for deeper understanding of ADHD traits and emotional dysregulation, which may in turn lessen the presence of rejection sensitivity in ADHD individuals.
PanEcho: Complete AI-enabled echocardiography interpretation with multi-task deep learning
Authors: Holste Gregory , Oikonomou Evangelos K. , Wang Zhangyang , Khera Rohan
Published: November 2024

Echocardiography is a mainstay of cardiovascular care offering non-invasive, low-cost, increasingly portable technology to characterize cardiac structure and function1. Artificial intelligence (AI) has shown promise in automating aspects of medical image interpretation2,3, but its applications in echocardiography have been limited to single views and isolated pathologies4–7. To bridge this gap, we presentPanEcho, a view-agnostic, multi-task deep learning model capable of simultaneously performing 39 diagnostic inference tasks from multi-view echocardiography.PanEchowas trained on >1 million echocardiographic videos with broad external validation across an internal temporally distinct and two external geographically distinct sets. It achieved a median area under the receiver operating characteristic curve (AUC) of 0.91 across 18 diverse classification tasks and normalized mean absolute error (MAE) of 0.13 across 21 measurement tasks spanning chamber size and function, vascular dimensions, and valvular assessment.PanEchoaccurately estimates left ventricular (LV) ejection fraction (MAE: 4.4% internal; 5.5% external) and detects moderate or greater LV dilation (AUC: 0.95 internal; 0.98 external) and systolic dysfunction (AUC: 0.98 internal; 0.94 external), severe aortic stenosis (AUC: 0.99), among others.PanEchois a uniquely view-agnostic, multi-task, open-source model that enables state-of-the-art echocardiographic interpretation across complete and limited studies, serving as an efficient echocardiographic foundation model.