bluuebunny/crossref_metadata_2025_split huggingface dataset:
AI As the New Age Estimator: Pioneering Customized Facial Surgery Outcomes
Authors: Alameddine Khaled , Rames Jess , Bakri Karim , Mardini Samir 
Published: April 2024

Abstract

Goals/Purpose
The imperative for precision in aesthetic surgery necessitates a robust framework for evaluating the impact of facial interventions on perceived age. Our study introduces a cutting-edge AI model aimed at discerning an individual's perceived age from facial characteristics. This tool is designed to augment the assessment of various plastic surgery procedures, facilitating the tailoring of interventions to each patient's unique facial aging pattern.

Methods/Technique
We harnessed a deep convolutional neural network (DCNN), pre-trained on the extensive ImageNet dataset, and further refined using 523,051 pre-annotated facial images from the IMBD-WIKI database, normalized as per the Mathias et al. face detection paradigm. Faces were processed into a 299x299 pixel matrix, maintaining a 40% margin around the face for uniformity. The Xception architecture was employed for its advanced feature extraction capabilities. The model was refined and tested against a diverse set of 100 patient faces from the Mayo Clinic's database, categorized by demographic and procedural data. The AI model employed regression analysis and softmax probability for precise age estimation.

Results/Complications
The AI model exhibited a remarkable accuracy rate of 92.5% in age estimation for pre procedural patients, with a standard deviation of 3.2 years. It significantly outperformed traditional methods in identifying fine-grained age-related features. The AI model discerned an average perceived age reduction of 3.5 years across all patients post-procedure, with a notable variance among different types of surgeries. Certain procedures, such as rhytidectomy and blepharoplasty, showed a more pronounced age-reduction effect.

Conclusion
The AI model presents an accurate and objective method for quantifying perceived age, serving as a significant benchmark in facial aesthetic evaluation. By illustrating measurable age reduction following various procedures, with some surgeries yielding more substantial changes in perceived age, the model stands as a testament to the effectiveness of plastic surgery interventions. The precision of our model in predicting age pre- and post-procedure underscores its potential to assist surgeons in custom-tailoring surgeries to individual aging patterns. This innovation is poised to refine the decision-making process in aesthetic surgery, ensuring treatments are aligned with the desired outcomes for rejuvenation and patient-specific needs, ultimately advancing the frontier of personalized plastic surgery.
Text-mining-based feature selection for anticancer drug response prediction
Authors: Wu Grace , Zaker Arvin , Ebrahimi Amirhosein , Tripathi Shivanshi , Mer Arvind Singh 
Published: April 2024

AbstractMotivationPredicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes.ResultsIn this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction.Availability and implementationhttps://github.com/merlab/text_features.
Literature mining discerns latent disease–gene relationships
Authors: Rai Priyadarshini , Jain Atishay , Kumar Shivani , Sharma Divya , Jha Neha , Chawla Smriti , Raj Abhijit , Gupta Apoorva , Poonia Sarita , Majumdar Angshul , Chakraborty Tanmoy , Ahuja Gaurav , Sengupta Debarka 
Published: April 2024

Abstract

Motivation
Dysregulation of a gene’s function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene–pathology relationships is an ever-daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape project, researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger-tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite diverse and is confounded by several clinical and demographic covariates.

Results
To circumvent this, we mined ∼18 million PubMed abstracts published till May 2019 and automatically selected ∼4.5 million of them that describe roles of particular genes in disease pathogenesis. Further, we fine-tuned the pretrained bidirectional encoder representations from transformers (BERT) for language modeling from the domain of natural language processing to learn vector representation of entities such as genes, diseases, tissues, cell-types, etc., in a way such that their relationship is preserved in a vector space. The repurposed BERT predicted disease–gene associations that are not cited in the training data, thereby highlighting the feasibility of in silico synthesis of hypotheses linking different biological entities such as genes and conditions.

Availability and implementation
PathoBERT pretrained model: https://github.com/Priyadarshini-Rai/Pathomap-Model. BioSentVec-based abstract classification model: https://github.com/Priyadarshini-Rai/Pathomap-Model. Pathomap R package: https://github.com/Priyadarshini-Rai/Pathomap.
BioCoder: a benchmark for bioinformatics code generation with large language models
Authors: Tang Xiangru , Qian Bill , Gao Rick , Chen Jiakang , Chen Xinyun , Gerstein Mark B 
Published: April 2024

Abstract

Summary
Pretrained large language models (LLMs) have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks and to be appropriately specialized to particular domains. Here, we target bioinformatics due to the amount of domain knowledge, algorithms, and data operations this discipline requires. We present BioCoder, a benchmark developed to evaluate LLMs in generating bioinformatics-specific code. BioCoder spans much of the field, covering cross-file dependencies, class declarations, and global variables. It incorporates 1026 Python functions and 1243 Java methods extracted from GitHub, along with 253 examples from the Rosalind Project, all pertaining to bioinformatics. Using topic modeling, we show that the overall coverage of the included code is representative of the full spectrum of bioinformatics calculations. BioCoder incorporates a fuzz-testing framework for evaluation. We have applied it to evaluate various models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, GPT-3.5, and GPT-4. Furthermore, we fine-tuned one model (StarCoder), demonstrating that our training dataset can enhance the performance on our testing benchmark (by &gt;15% in terms of Pass@K under certain prompt configurations and always &gt;3%). The results highlight two key aspects of successful models: (i) Successful models accommodate a long prompt (&gt;2600 tokens) with full context, including functional dependencies. (ii) They contain domain-specific knowledge of bioinformatics, beyond just general coding capability. This is evident from the performance gain of GPT-3.5/4 compared to the smaller models on our benchmark (50% versus up to 25%).

Availability and implementation
All datasets, benchmark, Docker images, and scripts required for testing are available at: https://github.com/gersteinlab/biocoder and https://biocoder-benchmark.github.io/.
Apilimod dimesylate in C9orf72 amyotrophic lateral sclerosis: a randomized phase 2a clinical trial
Authors: Babu Suma , Nicholson Katharine A , Rothstein Jeffrey D , Swenson Andrea , Sampognaro Paul J , Pant Pravin , Macklin Eric A , Spruill Susan , Paganoni Sabrina , Gendron Tania F , Prudencio Mercedes , Petrucelli Leonard , Nix Darrell , Landrette Sean , Nkrumah Esther , Fandrick Keith , Edwards Joan , Young Peter R 
Published: April 2024

Abstract
Apilimod dimesylate is a first-in-class phosphoinositide kinase, FYVE-type zinc finger-containing (PIKfyve) inhibitor with a favourable clinical safety profile and has demonstrated activity in preclinical C9orf72 and TDP-43 amyotrophic lateral sclerosis (ALS) models. In this ALS clinical trial, the safety, tolerability, CNS penetrance and modulation of pharmacodynamic target engagement biomarkers were evaluated.
This phase 2a, randomized, double-blind, placebo-controlled, biomarker-end-point clinical trial was conducted in four US centres (ClinicalTrials.gov NCT05163886). Participants with C9orf72 repeat expansions were randomly assigned (2:1) to receive twice-daily oral treatment with 125 mg apilimod dimesylate capsules or matching placebo for 12 weeks, followed by a 12-week open-label extension. Safety was measured as the occurrence of treatment-emergent or serious adverse events attributable to the study drug and tolerability at trial completion or treatment over 12 weeks. Changes from baseline in plasma and CSF and concentrations of apilimod dimesylate and its active metabolites and of pharmacodynamic biomarkers of PIKfyve inhibition [soluble glycoprotein nonmetastatic melanoma protein B (sGPNMB) upregulation] and disease-specific CNS target engagement [poly(GP)] were measured.
Between 16 December 2021 and 7 July 2022, 15 eligible participants were enrolled. There were no drug-related serious adverse events reported in the trial. Fourteen (93%) participants completed the double-blind period with 99% dose compliance [n = 9 (90%) apilimod dimesylate; n = 5 (100%) placebo]. At Week 12, apilimod dimesylate was measurable in CSF at 1.63 ng/ml [standard deviation (SD): 0.937]. At Week 12, apilimod dimesylate increased plasma sGPNMB by &gt;2.5-fold (P &lt; 0.001), indicating PIKfyve inhibition, and lowered CSF poly(GP) protein levels by 73% (P &lt; 0.001), indicating CNS tissue-level proof of mechanism.
Apilimod dimesylate met prespecified key safety and biomarker end-points in this phase 2a trial and demonstrated CNS penetrance and pharmacodynamic target engagement. Apilimod dimesylate was observed to result in the greatest reduction in CSF poly(GP) levels observed to date in C9orf72 clinical trials.
Adaptive coding of reward in schizophrenia, its change over time and relationship to apathy
Authors: Kaliuzhna Mariia , Carruzzo Fabien , Kuenzi Noémie , Tobler Philippe N , Kirschner Matthias , Geffen Tal , Katthagen Teresa , Böge Kerem , Zierhut Marco M , Schlagenhauf Florian , Kaiser Stefan 
Published: April 2024

Abstract
Adaptive coding of reward is the process by which neurons adapt their response to the context of available compensations. Higher rewards lead to a stronger brain response, but the increase of the response depends on the range of available rewards. A steeper increase is observed in a narrow range and a more gradual slope in a wider range. In schizophrenia, adaptive coding appears to be affected in different domains, especially in the reward domain. Here, we tested adaptive coding of reward in a large group of patients with schizophrenia (n = 86) and control subjects (n = 66). We assessed: (i) the association between adaptive coding deficits and symptoms; (ii) the longitudinal stability of deficits (the same task was performed 3 months apart); and (iii) the stability of results between two experimental sites.
We used functional MRI and the monetary incentive delay task to assess adaptation of participants to two different reward ranges: a narrow range and a wide range. We used a region-of-interest analysis to evaluate adaptation within striatal and visual regions. Patients and control subjects underwent a full demographic and clinical assessment.
We found reduced adaptive coding in patients, with a decreased slope in the narrow reward range with respect to that of control participants, in striatal but not visual regions. This pattern was observed at both research sites. Upon retesting, patients increased their narrow-range slopes, showing improved adaptive coding, whereas control subjects slightly reduced them. At retesting, patients with overly steep slopes in the narrow range also showed higher levels of negative symptoms.
Our data confirm deficits in reward adaptation in schizophrenia and reveal an effect of practice in patients, leading to improvement, with steeper slopes upon retesting. However, in some patients, an excessively steep slope may result in poor discriminability of larger rewards, owing to early saturation of the brain response. Together, the loss of precision of reward representation in new (first exposure, underadaptation) and more familiar (retest, overadaptation) situations might contribute to the multiple motivational symptoms in schizophrenia.
Adjunctive treatments for pneumococcal meningitis: a systematic review of experimental animal models
Authors: Koning Rutger , van Roon Marian A , Brouwer Matthijs C , van de Beek Diederik 
Published: April 2024

Abstract
New treatments are needed to improve the prognosis of pneumococcal meningitis. We performed a systematic review on adjunctive treatments in animal models of pneumococcal meningitis in order to identify treatments with the most potential to progress to clinical trials. Studies testing therapy adjunctive to antibiotics in animal models of pneumococcal meningitis were included. A literature search was performed using Medline, Embase and Scopus for studies published from 1990 up to 17 February 2023. Two investigators screened studies for inclusion and independently extracted data. Treatment effect was assessed on the clinical parameters disease severity, hearing loss and cognitive impairment and the biological parameters inflammation, brain injury and bacterial load. Adjunctive treatments were evaluated by their effect on these outcomes and the quality, number and size of studies that investigated the treatments. Risk of bias was assessed with the SYRCLE risk of bias tool. A total of 58 of 2462 identified studies were included, which used 2703 experimental animals. Disease modelling was performed in rats (29 studies), rabbits (13 studies), mice (12 studies), gerbils (3 studies) or both rats and mice (1 study). Meningitis was induced by injection of Streptococcus pneumoniae into the subarachnoid space. Randomization of experimental groups was performed in 37 of 58 studies (64%) and 12 studies (12%) were investigator-blinded. Overall, 54 treatment regimens using 46 adjunctive drugs were evaluated: most commonly dexamethasone (16 studies), daptomycin (5 studies), complement component 5 (C5; 3 studies) antibody and Mn(III)tetrakis(4-benzoicacid)porphyrin chloride (MnTBAP; 3 studies). The most frequently evaluated outcome parameters were inflammation [32 studies (55%)] and brain injury [32 studies (55%)], followed by disease severity [30 studies (52%)], hearing loss [24 studies (41%)], bacterial load [18 studies (31%)] and cognitive impairment [9 studies (16%)]. Adjunctive therapy that improved clinical outcomes in multiple studies was dexamethasone (6 studies), C5 antibodies (3 studies) and daptomycin (3 studies). HMGB1 inhibitors, matrix metalloproteinase inhibitors, neurotrophins, antioxidants and paquinimod also improved clinical parameters but only in single or small studies. Evaluating the treatment effect of adjunctive therapy was complicated by study heterogeneity regarding the animal models used and outcomes reported. In conclusion, 24 of 54 treatment regimens (44%) tested improved clinically relevant outcomes in experimental pneumococcal meningitis but few were tested in multiple well-designed studies. The most promising new adjunctive treatments are with C5 antibodies or daptomycin, suggesting that these drugs could be tested in clinical trials.
Weight, Anthropometric and Metabolic Changes After Discontinuing Antiretroviral Therapy Containing Tenofovir Alafenamide (TAF) in People With Human Immunodeficiency Virus (HIV)
Authors: Damas José , Munting Aline , Fellay Jacques , Haerry David , Marzolini Catia , Tarr Philip E , Steffen Ana , Braun Dominique L , Stoeckle Marcel , Bernasconi Enos , Tshikung Olivier Nawej , Fux Christoph A , Darling Katharine E A , Béguelin Charles , Wandeler Gilles , Cavassini Matthias , Surial Bernard ,   the Swiss HIV Cohort Study (SHCS), Abela I , Aebi-Popp K , Anagnostopoulos A , Battegay M , Bernasconi E , Braun D L , Bucher H C , Calmy A , Cavassini M , Ciuffi A , Dollenmaier G , Egger M , Elzi L , Fehr J , Fellay J , Furrer H , Fux C A , Günthard H F , Hachfeld A , Haerry D , Hasse B , Hirsch H H , Hoffmann M , Hösli I , Huber M , Jackson-Perry D , Kahlert C R , Kaiser L , Keiser O , Klimkait T , Kouyos R D , Kovari H , Kusejko K , Labhardt N , Leuzinger K , Martinez de Tejada B , Marzolini C , Metzner K J , Müller N , Nemeth J , Nicca D , Notter J , Paioni P , Pantaleo G , Perreau M , Rauch A , Salazar-Vizcaya L , Schmid P , Speck R , Stöckle M , Tarr P , Trkola A , Wandeler G , Weisser M , Yerly S 
Published: April 2024

Abstract

Background
Antiretroviral therapy (ART)-related weight gain is of particular concern in people with HIV (PWH). Although weight gain was observed among PWH receiving tenofovir alafenamide (TAF), little is known about the potential reversibility after TAF discontinuation. We evaluated weight and metabolic changes 12 months after TAF discontinuation in the Swiss HIV Cohort Study.

Methods
We included participants who received at least 6 months of TAF-containing ART between January 2016 and March 2023. Using multivariable mixed-effect models, changes in weight and lipid levels were compared between individuals who continued TAF and those who switched to one of the following TAF-free regimens: (1) tenofovir disoproxil fumarate (TDF)-based ART, (2) dolutegravir/lamivudine (DTG/3TC), or (3) long-acting cabotegravir/rilpivirine (CAB/RPV).

Results
Of 6555 participants (median age 54 years, 24.3% female, 13% Black), 5485 (83.7%) continued, and 1070 (16.3%) stopped TAF. Overall, discontinuing TAF was associated with an adjusted mean weight change of −0.54 kg (95% confidence interval [CI] −.98 to −.11) after 12 months. In stratified analyses, switching from TAF to TDF led to an adjusted mean weight decrease of −1.84 kg (95% CI −2.72 to −.97), and to a decrease in mean total cholesterol (−0.44 mmol/L) and triglycerides (−0.38 mmol/L) after 12 months. Switching from TAF-based ART to DTG/3TC (−0.17 kg, 95% CI −.82 to .48) or long-acting CAB/RPV (−0.64 kg, 95% CI −2.16 to .89) did not lead to reductions in weight.

Conclusions
Replacing TAF with TDF in PWH led to a decrease in body weight and an improved lipid profile within 1 year. Weight changes were not observed among individuals who switched to DTG/3TC or long-acting CAB/RPV.
The cybersecurity of fairy tales
Authors: Viganò Luca 
Published: April 2024

Abstract
The timeless nature of fairy tales can help uncover the timeless nature of many cybersecurity notions. Using 16 classical tales as illustration, in this article, the author shows that fairy tales are full of cybersecurity motifs and examples, and can thus be used extensively and effectively to explain cybersecurity notions. Obviously not cybersecurity as we know it today, but rather the fundamental and archetypal concepts that are at its heart. Fairy tales can thus be employed to mediate in conversations with less-knowledgeable, misinformed, or even skeptical laypersons about cybersecurity, increase their understanding and awareness, and thereby strengthen the overall cybersecurity of systems and applications.
Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography
Authors: Nurmohamed Nick S , van Rosendael Alexander R , Danad Ibrahim , Ngo-Metzger Quyen , Taub Pam R , Ray Kausik K , Figtree Gemma , Bonaca Marc P , Hsia Judith , Rodriguez Fatima , Sandhu Alexander T , Nieman Koen , Earls James P , Hoffmann Udo , Bax Jeroen J , Min James K , Maron David J , Bhatt Deepak L 
Published: April 2024

Abstract
Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individual’s complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation.
Younger but sicker? Cohort trends in disease accumulation among middle-aged and older adults in Scotland using health-linked data from the Scottish Longitudinal Study
Authors: Ribe Eloi , Cezard Genevieve Isabelle , Marshall Alan , Keenan Katherine 
Published: April 2024

Abstract

Background
In the United Kingdom, rising prevalence of multimorbidity—the co-occurrence of two or more chronic conditions- is coinciding with stagnation in life expectancy. We investigate patterns of disease accumulation and how they vary by birth cohort, social and environmental inequalities in Scotland, a country which has long suffered from excess mortality and poorer health outcomes relative to its neighbours.

Methods
Using a dataset which links census data from 1991, 2001 and 2011 to disease registers and hospitalization data, we follow cohorts of adults aged 30–69 years for 18 years. We model physical and mental disease accumulation using linear mixed-effects models.

Results
Recent cohorts experience higher levels of chronic disease accumulation compared to their predecessors at the same ages. Moreover, in more recently born cohorts we observe socioeconomic status disparities emerging earlier in the life course, which widen over time and with every successive cohort. Patterns of chronic conditions are also changing, and the most common diseases suffered by later born cohorts are cancer, hypertension, asthma, drug and alcohol problems and depression.

Conclusion
We recommend policies which target prevention of chronic disease in working age adults, considering how and why certain conditions are becoming more prevalent across time and space.