bluuebunny/crossref_metadata_2025 huggingface dataset:
Rectal Eversion as an Anus-sparing Technique in Laparoscopic Low Anterior Resection With Double Stapling Anastomosis: Long-term Functional Results
Authors: Karagul Servet , Senol Serdar , Karakose Oktay , Eken Huseyin , Kayaalp Cuneyt
Published: November 2024

Background:

Rectal eversion (RE) is a natural orifice specimen extraction (NOSE) method that allows anus-sparing resection in very low rectal tumors. This study aims to share the long-term results of RE in laparoscopic rectal resection performed with double stapling anastomosis.

Materials and Methods:

A single-center retrospective cohort study was conducted for patients who underwent laparoscopic low anterior resection with RE. Age, sex, body mass index, American Society of Anesthesiologists (ASA) classification, type of surgery, distance of the tumor to the dentate line, specimen extraction site, cancer stage, preoperative chemoradiotherapy, postoperative complications, and postoperative clinical follow-up findings were recorded. Incontinence was assessed using the Wexner score (WS). Low anterior resection syndrome (LARS) is determined by the LARS score. A 7-point Likert scale was used to evaluate the satisfaction of the patients.

Results:

A total of 17 patients underwent resection by RE for rectal tumors. Of the 11 patients included in the study, 4 were female and 7 were male. The mean age was 66.09±15.04 years. The mean follow-up was 64.18±16.83 months. The mean tumor diameter was 3.1 cm (range: 0.9 to 7.2 cm). The mean distance of the tumor from the dentate line was 2.7 cm (range: 1.2 to 5.6 cm). No anastomotic leak was observed in any patient. One patient had an anastomotic stenosis and was treated with balloon dilatation. The median LARS score was 16 (range 0 to 32) and 64% of the patients had no LARS. Two patients had minor LARS and 2 patients had major LARS. The median Wexner score was 3.5 (range 0 to 14). The median Likert scale was 7 (range 5 to 7). It was found that 55% of the patients were extremely satisfied, 18% were satisfied, and 27% were slightly satisfied with their surgery. There were no dissatisfied patients.

Conclusion:

RE is a safe NOSE technique in laparoscopic double stapling anastomosis for rectal resection. There is a high level of long-term patient satisfaction with anus-sparing procedures via RE, even in the presence of various symptoms.
Complete intraureteral stent placement reduces stent-related pain in normotensive subjects and in diabetes: Subgroup analysis of a prospective randomized clinical trial
Authors: Lu Yu-Cheng , Hsu Hung-Jen , Hu Su-Wei , Tzou Kai-Yi , Chen Kuan-Chou , Wu Chia-Chang , Ho Chen-Hsun
Published: November 2024

Purpose:

Pain is a common symptom that is associated with ureteral stent placement, but its degree of perception might vary among individuals. Compared with conventional stent placement (C-SP), complete intraureteral stent placement (CIU-SP) has shown promise in reducing stent-associated pain. This study aimed to investigate the effect of patient characteristics and comorbidities on pain reduction after CIU-SP.

Materials and methods:

Patients who underwent ureteroscopic lithotripsy and stent placement were randomized into the CIU-SP and C-SP groups. Stent-associated symptoms were evaluated using the Ureteral Stent Symptom Questionnaire on day 7, immediately before stent removal. Stent-associated pain was determined by the answer to question P1 (yes or no). Subgroup analysis was conducted based on various patient characteristics and comorbidities.

Results:

Of the 103 randomized patients, 91 (45 in CIU-SP and 46 in C-SP) were included in the final analysis. Body pain was reported by 31 (68.9%) after CIU-SP and 41 (89.1%) after C-SP (P = 0.021). Compared with C-SP, CIU-SP significantly reduced pain in the normotensive population (66.7% versus 93.7%, P = 0.006) but not in hypertensive individuals (77.8% versus 76.9%, P = 1.000). Conversely, compared with C-SP, CIU-SP was associated with less pain in patients with diabetes (37.5% versus 88.9%, P = 0.05) but not in patients without diabetes (75.7% versus 89.2%, P = 0.221). Age, sex, stone laterality, body mass index, and smoking status did not influence the reduction of pain after CIU-SP.

Conclusion:

Compared with C-SP, CIU-SP was effective in reducing stent-associated pain, especially in normotensive and diabetic populations. These findings align with existing literature, which emphasized the potential impact of hypertension on decreased pain perception and highlighted the known association between diabetes and bladder hypersensitivity.
2023 expert consensus on decision pathway of metastatic hormone-sensitive prostate cancer management in Taiwan
Authors: Chang Yen-Hwa , Tombal Bertrand , Chueh Jeff Shih-Chieh , Wu Chun-Te , Yu Chia-Cheng , Huang Chi-Ping , Wang Shian-Shiang , Huang Shu-Pin , Lin Victor Chia-Hsiang , Tsai Yuh-Shyan , Cheng Jason Chia-Hsien , Tsai Yu-Chieh , Pang See-Tong
Published: November 2024

This short communication presents a consensus-based expert opinion from a multidisciplinary team of Taiwanese experts in urology, medical oncology, and radiation oncology regarding the management of metastatic hormone-sensitive prostate cancer (mHSPC). Through a comprehensive review of recent clinical trials examining the efficacy of novel hormonal agents (NHAs) and docetaxel, alongside updated guidelines for mHSPC management, the experts have deliberated on the practical implications from currently available literatures. The discussion focused on the combination of androgen deprivation therapy, NHAs, and docetaxel, as reported in the leading studies including ARASENS (ODM-201 in Addition to Standard ADT and Docetaxel in Metastatic Castration Sensitive Prostate Cancer), ENZAMET (Enzalutamide in First Line Androgen Deprivation Therapy for Metastatic Prostate Cancer), and PEACE-1 (A Phase III Study for Patients with Metastatic Hormone naive Prostate Cancer) trial. The risk-benefit ratio between prolonged overall survival and the incidence of major adverse events was thoroughly evaluated. This consensus underscores the recommendation that early initiation of intensified triplet therapy with docetaxel, NHA, and androgen deprivation therapy is beneficial for fit mHSPC patients with highly aggressive disease characteristics, effectively prolonging overall survival with manageable risk of adverse events.
Detection of ERG11 gene mutation in coding and non-coding region of clinical Candida glabrata isolates from Pakistan, a short communication
Authors: Memon Saba , Ghanchi Najia Karim , Zafar Urooj , Farooqi Joveria , Zaka Sadaf , Jabeen Kauser
Published: November 2024

Azoles inhibit cytochrome P450 dependent enzyme lanosterol-14α-demethylase (CYP51) that is encoded by ERG11gene. Mutations in the ERG11gene confer azole resistance in Candida species through different mechanisms, like ERG11 overexpression, CDR1,2 overexpression, tolerance to methylation, biofilm formation and import of host cholesterol. We sequenced ERG11 gene to determine mutations in coding and non-coding region of ERG11 in clinical isolates of Candida glabrata from Pakistan. Eight C. glabrata strains with fluconazole minimum inhibitory concentrations ranging from 2-256 mg/L were included in this study. The ERG11 gene was amplified by PCR and sequenced using Sanger methodology and analyzed using bioinformatic tools. We identified non-synonymous mutation at position I166S in non-coding region of ERG11gene in C. glabrata. Two novel synonymous mutations at position I100 and D278, and previously reported synonymous mutations D256, L341, P519 and R527 were also observed. This is the first study evaluating ERG11 mutations in C. glabrata species from Pakistan. In this study, we reported I166S mutation in non-coding region of ERG11 gene. Such ERG11 gene mutations which are unique to geographical regions should be investigated through resistance and genotypic surveillance.
Effectiveness of Traditional Herbal Extracts Against Multidrug-Resistant Bacteria: A Review
Authors: Pane Yunita Sari
Published: November 2024

The rise of multidrug-resistant (MDR) bacteria has highlighted an urgent need for alternative antibacterial therapies. Traditional herbal extracts offer potential as natural antimicrobial agents with fewer side effects compared to conventional antibiotics. This review examines the effectiveness of specific herbal extracts—Curcuma longa (turmeric), Opuntia ficus-indica (cactus), and Linum usitatissimum (flaxseed)—against MDR bacterial strains. Data from various studies indicate that these extracts, particularly in methanolic and aqueous forms, exhibit inhibitory effects on pathogens such as Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae. Notably, C. longa methanolic extracts showed the strongest antibacterial activity across strains, while O. ficus-indica extracts were effective in autoclaved forms against resistant strains. Comparatively, L. usitatissimum displayed minimal direct antibacterial action but may hold promise in combined therapies. This review also highlights synergistic effects observed when these herbal extracts are paired with conventional antibiotics, suggesting potential for reduced antibiotic dosages. Given the increasing antibiotic resistance, these findings support the integration of traditional herbal treatments as complementary options in antibacterial strategies. Future research should focus on optimizing extraction methods and evaluating the clinical applicability of these herbal alternatives in combating MDR pathogens.
Prediction of context-specific regulatory programs and pathways using interpretable deep learning
Authors: Doncevic Daria , Ramirez Alvarez Carlos , Li Albert , Zhang Youcheng , von Bachmann Anna , Noack Kasimir , Herrmann Carl
Published: November 2024

Variational autoencoders (VAEs) are being widely adopted for the analysis of single-cell RNA sequencing (scRNA-seq) data. As with any non-linear models, however, they lack interpretability, which is a crucial aspect in the biomedical field where researchers want to be able to trust their model predictions. Our previously developed OntoVAE model addressed this issue by integrating biological ontologies in the decoder, which made the neuronal activations correspond to pathway activities. However, when multiple covariates are present, disentangling their relative contributions is challenging. To address this limitation, we developed COBRA, a VAE tool that combines the interpretable decoder part of OntoVAE with an adversarial approach that separates covariate effects in the latent space. In this work, we demonstrate the use of COBRA on two different scRNA-seq datasets in different contexts. We applied the tool to an interferon stimulated mouse dataset to separate the effects of celltype and treatment on transcription factors and biological pathways. We furthermore showed how COBRA can be used to predict the state of unseen celltypes.
Mapping the Molecular Landscape of Thyroid Neoplasms: A Comprehensive Proteomic and Phosphoproteomic Analysis Across Tumors of Follicular Origin
Authors: Bossart Jonas , Tajbakhsh Kiarash , Stanowska Olga , Weber Achim , Zboray Robert , Perren Aurel , Buljan Marija
Published: November 2024

Thyroid nodules are a widespread phenomenon, with follicular cell-derived thyroid tumors being the most prevalent type of endocrine tumor, spanning from benign through low grade malignant to aggressive neoplasms with dismal prognosis. In clinical practice, histopathological criteria are primarily used to determine malignancy and aggressiveness. Therefore, accurate classification may result in surgical procedures for diagnostic reasons, associated with an imbalanced risk/benefit ratio. In recent years, the use of integrated proteomic approaches has proven valuable in expanding the molecular understanding of thyroid neoplasms, with implications in classification, yet remains understudied in divergent thyroid nodules. Here we show the delineation of subtype-specific and malignancy-dependent molecular characteristics through integrative proteomic and phosphoproteomic analysis of 53 human thyroid tissues, encompassing five frequent benign and malignant tumors. We found that the (phospho)-proteomic profiles enable a clear stratification of malignant and benign thyroid tissues. The method also performs well in delineating follicular adenoma (FA) and follicular thyroid carcinoma (FTC) samples. Beside the dysregulation of cell cycle control, apoptosis, and metabolic reprogramming associated with tumor development and malignancy, we further report increased alterations within the well-established oncogenic RAS/BRAF/MAPK and AKT/MTOR signaling pathways, which, contrary to the prevailing paradigm, did not clearly differentiate between FTC and papillary thyroid carcinoma (PTC). In addition, activities of ATM, PLK2-3, and GRK5-6 kinases were predicted to be strongly upregulated in malignant subtypes. Together, this study provides an in-depth insight into molecular changes in different thyroid tumor subtypes. These findings highlight the potential of integrated proteomic approaches to refine our understanding of complex diseases like cancer. As such, they offer a pathway to more precise diagnostic and personalized treatment strategies.
Epigenetic signatures of regional tau pathology and cognition in the aging and pathological brain
Authors: Goldberg David C. , Wadhwani Anil R. , Dehghani Nadia , Sreepada Lasya P. , Fu Hongxiang , De Jager Philip L. , Bennett David A. , Wolk David A. , Lee Edward B. ,   PART Working Group, Farrell Kurt , Crary John F. , Zhou Wanding , McMillan Corey T.
Published: November 2024

Primary age-related tauopathy (PART) and Alzheimer’s disease (AD) share hippocampal phospho-tau (p-tau) pathology but differ in p-tau extent and ß-amyloid presence. As a result, PART uniquely enables investigation of amyloid-independent p-tau mechanisms during brain aging. We conducted an epigenome-wide association (EWAS) study of PART, nominating 13 new and robust p-tau/methylation associations. We then jointly analyzed PART and AD epigenomes to develop novel epigenetic clocks, “TauAge”, that predict p-tau severity in region-specific, age-, and ß-amyloid-independent manners. Integrative transcriptomic analyses revealed that genes involved in synaptic transmission are related to hippocampal p-tau severity in both PART and AD, while neuroinflammatory genes are related to frontal cortex p-tau severity in AD only. Further, a machine learning classifier trained on PART-vs-AD epigenetic differences stratifies an independent cohort of neuropathologically indeterminate cases into pathological subgroups with disparity in cognitive impairment. Together, these findings demonstrate the brain epigenome’s substantial role in linking tau pathology to cognitive outcomes in aging and AD.
Integrating knowledge, omics and AI to develop patient-specific virtual avatars
Authors: Bekkar Amel , Santuari Luca , Xenarios Ioannis , Arpat Bulak
Published: November 2024

We propose a method for creating personalized regulatory networks, enabling the development of virtual avatars for cancer patients, with patient-derived xenograft (PDX) models as a test case. Starting from a Prior Knowledge Network (PKN) based on the hallmarks of cancer, we constructed gene networks that are contextualized to each sample by integrating sample-specific gene expression data. These networks were optimized using a genetic algorithm to align with individual molecular profiles, focusing on key cancer-related processes. Following network optimization, we employed Graph Convolutional Networks (GCNs) to classify samples based the structures and interactions of their individualized network models and molecular profiles. This personalized approach provides insights into drug responses and helps predict treatment outcomes, offering a path toward more targeted cancer therapies.

Author summary

Cancer treatment can be more effective when therapies are personalized to each patients unique molecular profile. In this study, we introduce a method to create virtual avatars of cancer patients by personalizing regulatory networks using patient-derived xenograft (PDX) models as a proof of concept. Starting from known cancer hallmarks, we developed individualized gene networks for each sample by leveraging their specific gene expression data. These networks were refined with an optimization process to match the distinct molecular characteristics of each sample. By applying advanced machine learning, specifically Graph Convolutional Networks (GCNs), we classified these personalized models to better understand likely drug responses and predict treatment outcomes. This approach brings us closer to tailoring cancer therapies to individual patients, potentially improving treatment success by targeting key cancer pathways unique to each person.
A National Active Case-Finding Program for Tuberculosis in Prisons, Peru, 2024
Authors: Jung Esther , Alarcón Valentina A. , Solís Tupes Wilfredo Santos , Avalos-Cruz Tatiana , Tovar Marco , Abregu Erika , Yang Max Z. , Andrews Jason R. , Huaman Moises A.
Published: November 2024

From January to September 2024, a national active case-finding program in Peru’s prisons screened more than 38,000 incarcerated persons for tuberculosis using chest radiography with automated interpretation and rapid molecular diagnostics, yielding a tuberculosis prevalence of 2,800 per 100,000 persons, with 11.4% of cases rifampicin-resistant; 42.5% of cases were subclinical.

Article Summary Line

A recent, nationwide mass screening program in Peru’s prisons using digital radiography with automated interpretation and molecular testing identified high tuberculosis prevalence and rates of rifampicin resistance, highlighting the need for active case-finding as an efficient strategy to identify undiagnosed TB burden in high-risk populations.
CircAge: A Comprehensive Resource for Aging-Associated Circular RNAs Across Species and Tissues
Authors: Dong Xin , Zhou Zhen , Wang Yanan , Nisar Ayesha , Pu Shaoyan , Lv Longbao , Li Yijiang , Lu Xuemei , He Yonghan
Published: November 2024

Circular RNAs (circRNAs) represent a novel class of RNA molecules characterized by a circular structure and enhanced stability. Emerging evidence indicates that circRNAs play pivotal regulatory roles in the aging process. Despite this, there is a lack of a systematic resource that integrates aging-associated circRNA data. Therefore, we developed a comprehensive database named circAge, which encompasses 689 aging-related samples from 7 species and 21 tissue types. We also generated 47 new tissue samples from mice and rhesus monkeys through high-throughput sequencing. Integrating predictions from multiple bioinformatics tools, we identified over 413,378 unique circRNAs. Our data analysis revealed a general increase in circRNA expression levels with age, with approximately 22% of circRNAs demonstrating sequence conservation across species. The circAge database systematically predicts potential interactions between circRNAs and miRNAs, RNA-binding proteins, and assesses the coding potential of circRNAs. This resource lays a foundation for elucidating the regulatory mechanisms of circRNAs in aging. As a comprehensive repository of aging-associated circRNAs, circAge will significantly accelerate research in this field, facilitating the discovery of novel biomarkers and therapeutic targets for aging biology and developing diagnostic and therapeutic strategies for aging and age-related diseases. CircAge is publicly available athttps://circage.kiz.ac.cn.
A simple method for computationally unstructuring proteins: some findings
Authors: Powell Alexander
Published: November 2024

A methodology for computationally ‘unstructuring’ proteins is described and the results of its application to a variety of proteins analyzed and discussed. Some proteins prove more susceptible than others, and fold topology plays a part in this. Alpha helical structure is found to be generally somewhat robust, and, perhaps unsurprisingly, unstructuring often begins at exposed chain termini. Phosphofructokinase-1 and phosphofructokinase-2, which have similar sizes but different fold topologies, are found to differ markedly in their unstructuring behaviour.

Subjects:biophysics, chemical biology, computational biology, protein science, structural biology
Towards a Social Media-based Disease Surveillance System for Early Detection of Influenza-like Illnesses: A Twitter Case Study inWales
Authors: Drakesmith Mark , Antypas Dimosthenis , Brown Clare , Camacho-Collados Jose , Song Jiao
Published: November 2024

Social media offers the potential to provide detection of outbreaks or public health incidents faster than traditional reporting mechanisms. In this paper, we developed and tested a pipeline to produce alerts of influenza-like illness (ILI) using Twitter data. Data was collected from the Twitter API, querying keywords referring to ILI symptoms and geolocated to Wales. Tweets that described first-hand descriptions of symptoms (as opposed to non-personal descriptions) were classified using transformer-based language models specialised on social media (BERTweet and TimeLMs), which were trained on a manually labelled dataset matching the above criteria. After gathering this data, weekly tweet counts were applied to the regression-based Noufaily algorithm to identify exceedances throughout 2022. The algorithm was also applied to counts of ILI-related GP consultations for comparison. Exceedance detection applied to the classified tweet counts produced alerts starting four weeks earlier than by using GP consultation data. These results demonstrate the potential to facilitate advanced preparedness for unexpected increases in healthcare burdens.
What incentives encourage local communities to collect and upload mosquito sound data by using smartphones? A case study in the Democratic Republic of the Congo
Authors: Storer Kieran E. , Messina Jane P. , Herreros-Moya Eva , Metelo Emery , Zanga Josué , Mvuama Nono M. , Muzinga Soleil , Dam Rinita , Sinka Marianne , Kiskin Ivan , Everett Josh , Li Yunpeng , Roberts Stephen , Willis Katherine J.
Published: November 2024

Malaria is one of the deadliest vector borne diseases effecting sub-Saharan Africa. A suite of systems are being used to monitor and manage malaria risk and disease incidence, with an increasing focus on technological interventions that allow private citizens to remotely record and upload data. However, data collected by citizen scientists must be standardised and consistent if it is to be used for scientific analysis. Studies that aim to improve data collection quality and quantity have often included incentivisation, providing citizen scientists with monetary or other benefits for their participation in data collection. We tested whether monetary incentives enhance participation and data collection in a study trialling an acoustic mosquito sensor. Working with the community in two health districts in the Democratic Republic of Congo, we measured data collection participation, completeness, and community responses. Our results showed mixed responses to the incentive, with more participants interested in the social status and monetary value of the technology used than the monetary incentive itself. The effect of incentives on data collection varied over the course of the trial, increasing participation in the start of the trial but with no effect in the latter half of the trial. Feedback from participants showed that opinions on technology, research objectives, and incentives varied between communities, and was associated with differences in data collection quantity and quality, suggesting that differences in community interest in data collection and the incentives may be more important than the incentive value itself. These results suggest that though there is an initial benefit, extrinsic motivations do not override differences in intrinsic motivations over time, and enhanced communication and dialogue with participants may improve citizen science participation and attitudes.

Attribution

KES contributed to data curation, formal analysis, methodology, visualization, and writing the original draft. JPM contributed to formal analysis and visualization. EHM contributed to conceptualization, investigation, methodology, project administration, supervision, visualization, and the review and editing of the manuscript. EM, JZ, NMM, and SM contributed to conceptualization, investigation, methodology, data collection, and review and editing of the manuscript. RD, MS, IK, JE, YL, and SR contributed to conceptualization, methodology, and review and editing of the manuscript. KJW contributed to funding acquisition, methodology, supervision, and the review and editing of the manuscript.

Funding

This study was funded by the Bill and Melinda Gates Foundation Grant Award Number: OPP1209888.
A multicentre prospective observational study to investigate the prevalence and short-term impact of frailty, multi-morbidity and sarcopenia in chronic limb threatening ischaemia (CLTI) The FraiLTI - (Frailty in chronic Limb Threatening Ischaemia) Study
Authors: Sivaharan Ashwin , Hickson Blaise , El-Sayed Tamer , Shelmerdine Lauren , Wunnava Sai , Baljer Bence , Bhullar Dilraj , Nesbitt Craig I , Witham Miles D , Nandhra Sandip
Published: November 2024

Background

CLTI is a is a life and limb threatening condition that is associated with a gradual overall decline in health that is under explored. We aim to measure the prevalence of correlated conditions (frailty, sarcopenia and anaemia) in patients with CLTI to assess their ability to perform activities of daily living (ADLs) alongside multimorbidity, polypharmacy and clinical outcomes.

Methods

FraiLTI is a multi-centre, prospective, observational study, funded by the National Institute for Health Research (NIHR) and supported by the Vascular and Endovascular Research (VERN). All dedicated vascular centres were eligible to participate. Ethical approval (21/PR/0750) was granted on 13/07/2021. Primary outcome was identification of prevalence rates of frailty, sarcopenia and anaemia. Secondary outcomes included prevalence of polypharmacy and multimorbidity. Analyses included associations between frailty and sarcopenia, with clinical outcomes.

Results

By the Rockwood Clinical Frailty Scale, 43 (51.8%) patients were frail. By grip strength, 27 (33.3%) patients were sarcopenic. We found that 43 (53.8%) of patients were anaemic, with an overwhelming majority (92.1%) having normocytic anaemia. Multimorbidity was seen in 57 (67.9%) patients and 67 (79.8%) patients met the definition of polypharmacy. Patients who were frail, were more likely to require a walking aid (p<0.001), be on more medications (p=0.027), have more co-morbidities (p=0.002), more likely to be diabetic (p=0.016), be sarcopenic (p=0.018), fail the walk speed test (p=0.040) and self-report low activity (p<0.001). Patients who were considered sarcopenic by grip strength tended to be older (p=0.031), have more co-morbidities (p=0.049), and were more likely to be anaemic (p=0.004).

Conclusion

This study shows that frailty, sarcopenia and anaemia are highly prevalent in those with CLTI. There also appears to be an important interaction between sarcopenia and anaemia, but not frailty and anaemia. The management of CLTI patients extends beyond revascularisation alone.
AI-Powered Test Question Generation in Medical Education: The DailyMed Approach
Authors: van Uhm J. , van Haelst M.M. , Jansen P.R.
Published: November 2024

Introduction

Large language models (LLMs) presents opportunities to improve the efficiency and quality of tools in medical education, such as the generation of multiple-choice questions (MCQs). However, ensuring that these questions are clinically relevant, accurate, and easily accesible and reusable remains challenging. Here, we developed DailyMed, an online automated pipeline using LLMs to generate high-quality medical MCQs.

Methods

Our DailyMed pipeline involves several key steps: 1) topic generation, 2) question creation, 3) validation using Semantic Scholar, 4) difficulty grading, 5) iterative improvement of simpler questions, and 6) final human review. The Chain-of-Thought (CoT) prompting technique was applied to enhance LLM reasoning. Three state-of the art LLMs—OpenBioLLM-70B, GPT-4o, and Claude 3.5 Sonnet—were evaluated within the area of clinical genetics, and the generated questions were rated by clinical experts for validity, clarity, originality, relevance, and difficulty.

Results

GPT-4o produced the highest-rated questions, excelling in validity, originality, clarity, and relevance. Although OpenBioLLM was more cost-efficient, it consistently scored lower in all categories. GPT-4o also achieved the greatest topic diversity (89.8%), followed by Claude Sonnet (86.9%) and OpenBioLLM (80.0%). In terms of cost and performance, GPT-4o was the most efficient model, with an average cost of $0.51 per quiz and a runtime of 16 seconds per question.

Conclusions

Our pipeline provides a scalable, effective and online-accessible solution for generating diverse, clinically relevant MCQs. GPT-4o demonstrated the highest overall performance, making it the preferred model for this task, while OpenBioLLM offers a cost-effective alternative.
Reproducibility of electroencephalography alpha band biomarkers for diagnosis of major depressive disorder
Authors: Hollenbenders Y. , Maier C. , Reichenbach A.
Published: November 2024

Major depressive disorder (MDD) and other psychiatric diseases can greatly benefit from objective decision support in diagnosis and therapy. Machine learning approaches based on electroencephalography (EEG) have the potential to serve as low-cost decision support systems. Despite the successful demonstration of this approach, contradictory findings regarding the diagnostic value of those biomarkers hamper their deployment in a clinical setting. Therefore, thereproducibilityandrobustnessof these biomarkers needs to be established first. We employ a multiverse analysis to systematically investigate variations in five data processing steps, which may be one source of contradictory findings. These steps are normalization, time-series segment length, biomarker from the alpha band, aggregation, and classification algorithm. Forreplicabilityof our results, we utilize two publicly available EEG data sets with eyes-closed resting-state data containing 16/19 MDD patients and 14/14 healthy control subjects. The diagnostic classifiers range from chance level up to 85%, dependent on dataset and combination of processing steps. We find a large influence of choice of processing steps and their combinations. However, only the biomarker has an overall significant effect on both datasets. We find one biomarker candidate that has shown arobustandreproduciblehigh performance for MDD diagnostic support, therelative centroid frequency. Overall, thereplicabilityof our findings with the two datasets is rather inconsistent. This study is a showcase for the advantages of employing a multiverse approach in EEG data analysis and advocates for larger, well-curated data sets to further neuroscience research that can be translated to clinical practice.
Single cell long read whole genome sequencing reveals somatic transposon activity in human brain
Authors: Izydorczyk Michal B , Kalef-Ezra Ester , Horner Dominic W , Zheng Xinchang , Holmes Nadine , Toffoli Marco , Sahin Zeliha Gozde , Han Yi , Mehta Heer H , Muzny Donna M , Ameur Adam , Sedlazeck Fritz J , Proukakis Christos
Published: November 2024

The advent of single cell DNA sequencing revealed astonishing dynamics of genomic variability, but failed at characterizing smaller to mid size variants that on the germline level have a profound impact. In this work we discover novel dynamics in three brains utilizing single cell long-read sequencing. This provides key insights into the dynamic of the genomes of individual cells and further highlights brain specific activity of transposable elements.
Extensive binding of uncharacterized human transcription factors to genomic dark matter
Authors: Razavi Rozita , Fathi Ali , Yellan Isaac , Brechalov Alexander , Laverty Kaitlin U. , Jolma Arttu , Hernandez-Corchado Aldo , Zheng Hong , Yang Ally W.H. , Albu Mihai , Barazandeh Marjan , Hu Chun , Vorontsov Ilya E. , Patel Zain M. ,   The Codebook Consortium, Kulakovskiy Ivan V. , Bucher Philipp , Morris Quaid , Najafabadi Hamed S. , Hughes Timothy R.
Published: November 2024

Most of the human genome is thought to be non-functional, and includes large segments often referred to as “dark matter” DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells. Nearly half of them associated strongly with known regulatory regions such as promoters and enhancers, often at conserved motif matches and co-localizing with each other. Surprisingly, the other half often associated with genomic dark matter, at largely unique sites, via intrinsic sequence recognition. Dozens of these, which we term “Dark TFs”, mainly bind within regions of closed chromatin. Dark TF binding sites are enriched for transposable elements, and are rarely under purifying selection. Some Dark TFs are KZNFs, which contain the repressive KRAB domain, but many are not: the Dark TFs also include known or potential pioneer TFs. Compiled literature information supports that the Dark TFs exert diverse functions ranging from early development to tumor suppression. Thus, our results sheds light on a large fraction of previously uncharacterized human TFs and their unappreciated activities within the dark matter genome.
scExplorer: A Comprehensive Web Server for Single-Cell RNA Sequencing Data Analysis
Authors: Herńandez-Galaz Sergio , Hernández-Oliveras Andrés , Villanelo Felipe , Lladser Alvaro , Martin Alberto J.M.
Published: November 2024

Single-cell RNA sequencing (scRNA-seq) has redefined our ability to investigate cellular heterogeneity at unprecedented resolution. However, the computational analysis required for scRNA-seq data interpretation remains a significant barrier for many researchers. We present scExplorer, an interactive web application that streamlines the analysis workflow from preprocessing through dimensional reduction to differential expression analysis. The platform provides an intuitive interface for real-time data exploration and visualization while maintaining compatibility with established analysis frameworks in both R and Python environments. scExplorer significantly reduces the technical knowledge and computational requirements needed to perform scRNA-seq analysis, enabling researchers to focus on biological insights rather than computational complexity. The application modular design ensures scalability and robust performance, making it a valuable tool for both small-scale and large-scale single-cell studies.

Graphical Abstract
Natural Language Prompts Guide the Design of Novel Functional Protein Sequences
Authors: Praljak Nikša , Yeh Hugh , Moore Miranda , Socolich Michael , Ranganathan Rama , Ferguson Andrew L.
Published: November 2024

The advent of natural language interaction with machines has ushered in new innovations in text-guided generation of images, audio, video, and more. In this arena, we introduceBiologicalMulti-ModalModel (BioM3), as a novel framework for designing functional proteins via natural language prompts. This framework integrates natural language with protein design through a three-stage process: aligning protein and text representations in a joint embedding space learned using contrastive learning, refinement of the text embeddings, and conditional generation of protein sequences via a discrete autoregressive diffusion model. BioM3 synthe-sizes protein sequences with detailed descriptions of the protein structure, lineage, and function from text annotations to enable the conditional generation of novel sequences with desired attributes through natural language prompts. We presentin silicovalidation of the model predictions for subcellular localization prediction, reaction classification, remote homology detection, scaffold in-painting, and structural plausibility, andin vivoandin vitroexperimental tests of natural language prompt-designed synthetic analogs of Src-homology 3 (SH3) domain proteins that mediate signaling in the Sho1 osmotic stress response pathway in baker’s yeast. BioM3 possesses state-of-the-art performance in zero-shot prediction and homology detection tasks, and generates proteins with native-like tertiary folds and wild-type levels of experimentally assayed function.
Relationships between migraine and interoception: normal accuracy but altered sensibility and behaviour
Authors: Jones Siobhan , Komes Jessica , Benzaquén Ester , Vuong Quoc C. , Sedley William
Published: November 2024

Background

Migraine is typically precipitated by unaccustomed changes in one’s internal state and/or external environment. Migraine symptoms largely involve increased, noxious, awareness of bodily changes and external stimuli. Links have been proposed between migraine and interoception (sensing and interpreting internal states), but direct evidence is limited.

Methods

Unmedicated, otherwise healthy, age-matched female participants were grouped by migraine tendency: control (no unprovoked headaches, n=19); low-frequency (<=3 migraines/month, n=20); high-frequency (>=4 migraines/month, n=19). Interoception was assessed, interictally, with standardised questionnaires such as MAIA-2 and a widely used heartbeat counting task.

Results

The notable significant questionnaire-based difference was in the ‘noticing’, ‘not distracting’ domain of the MAIA-2; controls were least likely to continue activities despite experiencing physical discomfort, and high-frequency migraineurs most likely. Follow-up questioning clarified that this behaviour related predominantly to migraine-related symptoms. The heartbeat task found no differences in accuracy, but lower confidence in the low-frequency migraine group than the control and high-frequency groups.

Conclusions

We suggest that low interoceptive confidence is a risk factor for migraine, whilst amplification of interoceptive signals caused by migraine restores this confidence, but at the price of migraine’s disabling symptoms. Self-reported tendency to deliberately ignore physical discomfort, including that caused by migraine, may result in more migraine attacks.
AlphaBind, a Domain-Specific Model to Predict and Optimize Antibody-Antigen Binding Affinity
Authors: Agarwal Aditya A. , Harrang James , Noble David , McGowan Kerry L. , Lange Adrian W. , Engelhart Emily , Lahman Miranda C. , Adamo Jeffrey , Yu Xin , Serang Oliver , Minch Kyle J. , Wellman Kimberly Y. , Younger David A. , Lopez Randolph M. , Emerson Ryan O.
Published: November 2024

Antibodies are versatile therapeutic molecules that utilize combinatorial sequence diversity to cover a vast fitness landscape. However, designing optimal antibody sequences remains a major challenge. Recent advances in deep learning provide opportunities to address this challenge by learning sequence-function relationships to accurately predict fitness landscapes. These models enable efficientin silicoprescreening and optimization of antibody candidates. By focusing experimental efforts on the most promising candidates guided by deep learning predictions, antibodies with optimal properties can be designed more quickly and effectively.

Here we present AlphaBind, a domain-specific model that utilizes protein language model embeddings and pre-training on millions of quantitative laboratory measurements of antibody-antigen binding strength to achieve state-of-the-art performance for guided affinity optimization of parental antibodies. We demonstrate that an AlphaBind-powered antibody optimization pipeline can deliver candidates with substantially improved binding affinity across four parental antibodies (some of which were already affinity-matured) and using two different types of training data. Resulting candidates, ranging up to 11 mutations from parental sequence, yield a sequence diversity that allows for optimization of other biophysical characteristics, all while using only a single round of data generation for each parental antibody. AlphaBind weights and code are publicly available at:https://github.com/A-Alpha-Bio/alphabind.
Climate change shapes richness-evenness relationships in a subalpine grassland experiment
Authors: Mühlbauer Lina K. , Klingler Andreas , Gaier Lukas , Schaumberger Andreas , Clark Adam Thomas
Published: November 2024

Grasslands face an uncertain future due to climate change. Although there is increased interest in the interdependencies of different biodiversity components, the effects of climate change on these relationships remains understudied. One of these is the richness-evenness relationship (RER), which is sensitive to altered species abundances in relation to richness. This relationship may be important as evenness and richness jointly shape diverse ecosystem functions, such as stability and productivity. As evenness effects productivity differently in low and high richness communities, the richness-evenness relationship is important to investigate, especially under climate change.

Here, we assess the effects of increased CO2concentrations, temperature, and drought on the RER in a subalpine long-term (2010 - ongoing) grassland climate change experiment, and test whether these effects can be buffered by reseeding. We provide evidence that climate change alters the RER in our experiment, and that these changes occur independently from changes in richness and evenness separately. Reseeding erases the differences in RER between treatments and controls but fails to restore the negative RER initially found in controls. Further, we show that the dominant grass species in our system(Arrhenatherum elatius)responds differently to each climate change factor, with opposite effects in high vs. low richness plots, thereby largely determining the direction of the RER.

These results suggest that the RER can reveal additional insights on community responses to climate change and represents a different signal than evenness or richness alone. A more nuanced approach integrating evenness and maximizing richness in seed mixtures could be an important step forward to better match restoration treatments to particular community types and global change drivers.
Graph Autoencoder and StrNN based Causal Analysis of Mortality in Heart Failure Patients
Authors: Dae young Kim
Published: November 2024

Though analyzed for decades, dissecting and finding mechanisms of cardiovascular diseases, especially heart failures, are still an on-going task for many researchers. However, through recent floods of machine learning and deep learning algorithms to replace traditional approaches, and their applications in diverse cardiovascular research areas, it seems plausible to say that conquering or preventing heart failure catastrophes might no longer be a delusional task within a few more years. To accelerate the arrival of a new era, this research implemented several cuttingedge algorithms currently introduced in causal deep learning to observational heart disease patient data to find key mechanisms that lead to cardiac deaths under a highly flexible framework. Extracting latent causal DAGs from observational data using Graph Auto Encoder, and finding specific causal relationships and interventional effects under Structured Neural Networks (StrNN), novel findings regarding key causes of deaths in heart failure patients were found in numerous aspects. Specifically, existence of intervals where average treatment effects due to causal interventions in platelets, ejection fraction, and serum creatinine levels dramatically decrease or increase was found among heart patients, which can lead to significant eliminations or additions of practical clinical treatments in terms of reducing cardiac death event probability after cardiac failure.
The contribution of mitochondria-associated ER membranes to cholesterol homeostasis
Authors: Montesinos J. , Kabra K. , Uceda M. , Larrea D. , Agrawal R.R. , Tamucci K.A. , Pera M. , Ferre A.C. , Gomez-Lopez N. , Yun T.D. , Velasco K.R. , Schon E.A. , Area-Gomez E.
Published: November 2024

Cellular demands for cholesterol are met by a balance between its biosynthesis in the endoplasmic reticulum (ER) and its uptake from lipoproteins. Cholesterol levels in intracellular membranes form a gradient maintained by a complex network of mechanisms including the control of the expression, compartmentalization and allosteric modulation of the enzymes that balance endogenous and exogenous sources of cholesterol. Low-density lipoproteins (LDLs) are internalized and delivered to lysosomal compartments to release their cholesterol content, which is then distributed within cellular membranes. High-density lipoproteins (HDLs), on the other hand, can transfer their cholesterol content directly into cellular membranes through the action of receptors such as the scavenger receptor B type 1 (SR-B1; geneSCARB1). We show here that SR-B1-mediated exogenous cholesterol internalization from HDL stimulates the formation of lipid-raft subdomains in the ER known as mitochondria-associated ER membranes (MAM), that, in turn, suppressde novocholesterol biosynthesis machinery. We propose that MAM is a regulatory hub for cholesterol homeostasis that offers a novel dimension for understanding the intracellular regulation of this important lipid.
Antimicrobial susceptibility testing ofClostridioides difficile: a dual-site study of three different media and three therapeutic antimicrobials
Authors: Freeman Jane , Sanders Ingrid M.J.G. , Harmanus Céline , Clark Emma V. , Berry Andrea M , Smits Wiep Klaas
Published: November 2024

Objectives

Increasing resistance to antimicrobials used for the treatment ofClostridioides difficileinfections necessitates reproducible antimicrobial susceptibility testing. Current guidelines take a one-size-fits-all approach and/or offer limited guidance. We investigated how the choice of medium affects measured MIC values across two sites.

Methods

We determined MIC values for the antimicrobials fidaxomicin, metronidazole and vancomycin for a representative collection of EuropeanC. difficilestrains (n=235) using agar dilution on three different media: Brucella Blood Agar (BBA), Fastidious Anaerobe Agar supplemented with horseblood (FAA-HB) and Wilkins-Chalgren (WC) agar. The study was conducted at two sites to compare reproducibility. Useability (ease of preparation of the media as well as read-out of the assay) was assessed through a survey.

Results

We found that all media result in highly consistent aggregated MIC data for all antibiotics, with MIC50and MIC90within 2-fold of each other across sites. For fidaxomin, MIC values on WC were lower than on the other media. Metronidazole showed the lowest MIC on BBA, and the highest on WC. For vancomycin, there was little difference between media. Though absolute values for individual isolates differed between sites, identified resistant isolates were similar. Results obtained on FAA-HB were most consistent between sites and results obtained on WC showed the most divergence. FAA-HB was positively evaluated in the usability survey.

Conclusions

This study shows medium-dependent differences inC. difficileMICs for at least two antimicrobials across two sites. We suggest the use of FAA-HB to align with general EUCAST recommendations for susceptibility testing of anaerobic bacteria and deposited reference strains for standard susceptibility testing ofC. difficileto increase interlaboratory reproducibility.