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Gene editing nucleases, base editors, and prime editors are potential locus-specific genetic treatment strategies for recessive dystrophic epidermolysis bullosa; however, many recessive dystrophic epidermolysis bullosa COL7A1 pathogenic nucleotide variations (PNVs) are unique, making the development of personalized editing reagents challenging. A total of 270 of the ∼320 COL7A1 epidermolysis bullosa PNVs reside in exons that can be skipped, and antisense oligonucleotides and gene editing nucleases have been used to create in-frame deletions. Antisense oligonucleotides are transient, and nucleases generate deleterious double-stranded DNA breaks and uncontrolled mixtures of allele products. We developed a twin prime editing strategy using the PEmax and recently evolved PE6 prime editors and dual prime editing guide RNAs flanking COL7A1 exon 5. Prime editing–mediated deletion of exon 5 with a homozygous premature stop codon was achieved in recessive dystrophic epidermolysis bullosa fibroblasts, keratinocytes, and induced pluripotent stem cells with minimal double-stranded DNA breaks, and collagen type VII protein was restored. Twin prime editing can replace the target exon with recombinase attachment sequences, and we exploited this to reinsert a normal copy of exon 5 using the Bxb1 recombinase. These findings demonstrate that twin prime editing can facilitate locus-specific, predictable, in-frame deletions and sequence replacement with few double-stranded DNA breaks as a strategy that may enable a single therapeutic agent to treat multiple recessive dystrophic epidermolysis bullosa patient cohorts., Gene editing nucleases, base editors, and prime editors are potential locus-specific genetic treatment strategies for recessive dystrophic epidermolysis bullosa; however, many recessive dystrophic epidermolysis bullosa COL7A1 pathogenic nucleotide variations (PNVs) are unique, making the development of personalized editing reagents challenging. A total of 270 of the ∼320 COL7A1 epidermolysis bullosa PNVs reside in exons that can be skipped, and antisense oligonucleotides and gene editing nucleases have been used to create in-frame deletions. Antisense oligonucleotides are transient, and nucleases generate deleterious double-stranded DNA breaks and uncontrolled mixtures of allele products. We developed a twin prime editing strategy using the PEmax and recently evolved PE6 prime editors and dual prime editing guide RNAs flanking COL7A1 exon 5. Prime editing–mediated deletion of exon 5 with a homozygous premature stop codon was achieved in recessive dystrophic epidermolysis bullosa fibroblasts, keratinocytes, and induced pluripotent stem cells with minimal double-stranded DNA breaks, and collagen type VII protein was restored. Twin prime editing can replace the target exon with recombinase attachment sequences, and we exploited this to reinsert a normal copy of exon 5 using the Bxb1 recombinase. These findings demonstrate that twin prime editing can facilitate locus-specific, predictable, in-frame deletions and sequence replacement with few double-stranded DNA breaks as a strategy that may enable a single therapeutic agent to treat multiple recessive dystrophic epidermolysis bullosa patient cohorts.

The dark photon is a promising candidate for the dark matter which comprises most of the matter in our visible Universe. Via kinetic mixing with the Standard Model it can also be resonantly converted to photons in an electromagnetic cavity, offering novel experimental possibilities for the discovery and study of dark matter. We report the results of a pathfinder dark photon dark matter cavity search experiment performed at Hunan Normal University and the Institute of Physics, Chinese Academy of Sciences, representing the first stage of the APEX (Axion and dark Photon EXperiment) program. Finding no statistically significant excess, we place an upper limit on the kinetic mixing parameter $|\chi|<3.7\times 10^{-13}$ around $m_A\simeq 29.5$ $\mu$eV at 90% confidence level. This result exceeds other constraints on dark photon dark matter in this frequency range by roughly an order of magnitude., Comment: 5 pages, 4 figures. Updated to match journal version

Possible light bosonic dark matter interactions with the Standard Model photon have been searched by microwave resonant cavities. In this paper, we demonstrate the cryogenic readout system calibration of a 7.138 GHz copper cavity with a loaded quality factor $Q_l=10^4$, operated at 22 mK temperature based on a dilution refrigerator. Our readout system consists of High Electron Mobility Transistors as cryogenic amplifiers at 4 K, plus room-temperature amplifiers and a spectrum analyzer for signal power detection. We test the system with a superconducting two-level system as a single-photon source in the microwave frequency regime and report an overall 95.6 dB system gain and -71.4 dB attenuation in the cavity's input channel. The effective noise temperature of the measurement system is 7.5 K., Comment: 7 pages, 5 figures, version to appear in CPC

Genetic variation at the 19q13.3 KLK locus is linked with prostate cancer susceptibility in men. The non-synonymous KLK3 single nucleotide polymorphism (SNP), rs17632542 (c.536 T > C; Ile163Thr-substitution in PSA) is associated with reduced prostate cancer risk, however, the functional relevance is unknown. Here, we identify that the SNP variant-induced change in PSA biochemical activity mediates prostate cancer pathogenesis. The ‘Thr’ PSA variant leads to small subcutaneous tumours, supporting reduced prostate cancer risk. However, ‘Thr’ PSA also displays higher metastatic potential with pronounced osteolytic activity in an experimental metastasis in-vivo model. Biochemical characterisation of this PSA variant demonstrates markedly reduced proteolytic activity that correlates with differences in in-vivo tumour burden. The SNP is associated with increased risk for aggressive disease and prostate cancer-specific mortality in three independent cohorts, highlighting its critical function in mediating metastasis. Carriers of this SNP allele have reduced serum total PSA and a higher free/total PSA ratio that could contribute to late biopsy decisions and delay in diagnosis. Our results provide a molecular explanation for the prominent 19q13.3 KLK locus, rs17632542 SNP, association with a spectrum of prostate cancer clinical outcomes.

We study a nearly critical superfluid system from two complementary approaches. Within the first approach, we formulate a Schwinger-Keldysh effective field theory (EFT) for the system when it is located slightly above the critical temperature. . The set of symmetries, particularly the dynamical Kubo-Martin-Schwinger (KMS) symmetry and chemical shift symmetry, strictly constrains the form of EFT action. Within the second approach, using the holographic Schwinger-Keldysh technique, we derive the effective action for a ``microscopic'' holographic superfluid, confirming the EFT construction. A systematic inclusion of non-Gaussianity is one highlight of present study., Comment: 35 pages

Xin Gao,1 Jiale Xu,2 Musen Xu,2 Pengzhe Han,2 Jingchao Sun,2 Ruifeng Liang,1 Shaojian Mo,2,&ast; Yanzhang Tian2,&ast; 1School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China; 2Department of Biliary and Pancreatic Surgery, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Shanxi Bethune Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shaojian Mo; Yanzhang Tian, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China, Tel +8619834514208 ; +8613903512030, Email 1009184648@qq.com; tyz2030@163.comPurpose: Acute pancreatitis is a common gastrointestinal emergency. Approximately 20% of patients with acute pancreatitis develop organ failure, which is significantly associated with adverse outcomes. This study aimed to establish an early prediction model for persistent organ failure in acute pancreatitis patients using 24-hour admission indicators.Patients and Methods: Clinical data and 24-h laboratory indicators of patients diagnosed with acute pancreatitis from January 1, 2017 to January 1, 2022 in Shanxi Bethune Hospital were collected. Patients from 2017 to 2021 were used as the training cohort to establish the prediction model, and patients from 2021 to 2022 were used as the validation cohort. Univariate logistic regression and LASSO regression were used to establish prediction models. The performance of the model was evaluated using area under the curve (AUC), calibration curves, and decision curve analysis (DCA), and subsequently validated in the validation group.Results: A total of 1166 patients with acute pancreatitis were included, a total of 145 patients suffered from persistent organ failure from 2017 to 2021. Data were initially selected for 100 variables, and after inclusion and exclusio

Using the Kreuzer-Skarke database of 4-dimensional reflexive polytopes, we systematically constructed a new database of orientifold Calabi-Yau threefolds with $h^{1,1}(X) \leq 12$. Our approach involved non-trivial $\mathbb{Z}_2$ involutions, incorporating both divisor exchanges and multi-divisor reflections acting on the Calabi-Yau threefolds. Each proper involution results in an orientifold Calabi-Yau threefolds and we constructed 320,386,067 such examples. We developed a novel algorithm that significantly reduces the complexity of determining all the fixed loci under the involutions, and clarifies the types of O-planes. Our results show that under proper involutions, the majority of cases end up with O3/O7-plane systems, and most of these further admit a naive Type IIB string vacua. Additionally, a new type of free action was determined. We also computed the smoothness and the splitting of Hodge numbers in the $\mathbb{Z}_2$-orbifold limit for these orientifold Calabi-Yau threefolds., Comment: 55 pages, 2 figures

Reactive distillation (RD) is a process intensification technique that offers major advantages over conventional technologies by enabling the integration of reaction and separation into a single apparatus. This integration introduces and exploits complex interaction between mass transfer, chemical reaction and hydrodynamics within an RD column; however, these complexities can hinder the adoption of RD by industry. Many approaches have been developed by the scientific community to advance understanding and to expedite the initialization of RD design at the conceptual level. This paper critically discusses recent developments in conceptual methods for synthesizing RD processes. This review paper is the first to consider the range of available approaches for assessing the technical feasibility, controllability, economic viability, and sustainability of RD units by taking into account various configurations in which RD is treated as a new unit operation, and as part of process synthesis in a different way of designing processes based on functions. The review also addresses complex configurations, such as advanced RD technologies. Special attention is paid to process modeling and simulation as well as to education. Knowledge gaps to be filled by further research are indicated., ChemE/Process Systems Engineering

Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems. Existing methods follow two main paradigms to collect evidence: (1) The \textit{retrieve-then-read} paradigm retrieves pertinent documents from an external corpus; and (2) the \textit{generate-then-read} paradigm employs large language models (LLMs) to generate relevant documents. However, neither can fully address multifaceted requirements for evidence. To this end, we propose LLMQA, a generalized framework that formulates the ODQA process into three basic steps: query expansion, document selection, and answer generation, combining the superiority of both retrieval-based and generation-based evidence. Since LLMs exhibit their excellent capabilities to accomplish various tasks, we instruct LLMs to play multiple roles as generators, rerankers, and evaluators within our framework, integrating them to collaborate in the ODQA process. Furthermore, we introduce a novel prompt optimization algorithm to refine role-playing prompts and steer LLMs to produce higher-quality evidence and answers. Extensive experimental results on widely used benchmarks (NQ, WebQ, and TriviaQA) demonstrate that LLMQA achieves the best performance in terms of both answer accuracy and evidence quality, showcasing its potential for advancing ODQA research and applications., Comment: TheWebConf 2024 (WWW 2024) oral, code repo: https://github.com/EthanLeo-LYX/LLMQA

Motivation Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances.Results To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements.Availability and implementation The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models in such domains. Earlier fine-tuning approaches sought to mitigate this by leveraging more precise supervisory signals from human labeling, larger models, or self-sampling, although at a high cost. Conversely, we develop a method that avoids external resources, relying instead on introducing perturbations to the input. Our training approach randomly masks certain tokens within the chain of thought, a technique we found to be particularly effective for reasoning tasks. When applied to fine-tuning with GSM8K, this method achieved a 5% improvement in accuracy over standard supervised fine-tuning with a few codes modified and no additional labeling effort. Furthermore, it is complementary to existing methods. When integrated with related data augmentation methods, it leads to an average improvement of 3% improvement in GSM8K accuracy and 1% improvement in MATH accuracy across five datasets of various quality and size, as well as two base models. We further investigate the mechanisms behind this improvement through case studies and quantitative analysis, suggesting that our approach may provide superior support for the model in capturing long-distance dependencies, especially those related to questions. This enhancement could deepen understanding of premises in questions and prior steps. Our code is available at Github.

The summarization capabilities of pretrained and large language models (LLMs) have been widely validated in general areas, but their use in scientific corpus, which involves complex sentences and specialized knowledge, has been less assessed. This paper presents conceptual and experimental analyses of scientific summarization, highlighting the inadequacies of traditional evaluation methods, such as $n$-gram, embedding comparison, and QA, particularly in providing explanations, grasping scientific concepts, or identifying key content. Subsequently, we introduce the Facet-aware Metric (FM), employing LLMs for advanced semantic matching to evaluate summaries based on different aspects. This facet-aware approach offers a thorough evaluation of abstracts by decomposing the evaluation task into simpler subtasks.Recognizing the absence of an evaluation benchmark in this domain, we curate a Facet-based scientific summarization Dataset (FD) with facet-level annotations. Our findings confirm that FM offers a more logical approach to evaluating scientific summaries. In addition, fine-tuned smaller models can compete with LLMs in scientific contexts, while LLMs have limitations in learning from in-context information in scientific domains. This suggests an area for future enhancement of LLMs., Comment: 14pages

Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews, often face challenges with engagement and accessibility. While game-based and LLM-based tools have been explored to improve user interest and automate assessment, they struggle to balance engagement with generalizability. In this work, we propose PsychoGAT (Psychological Game AgenTs) to achieve a generic gamification of psychological assessment. The main insight is that powerful LLMs can function both as adept psychologists and innovative game designers. By incorporating LLM agents into designated roles and carefully managing their interactions, PsychoGAT can transform any standardized scales into personalized and engaging interactive fiction games. To validate the proposed method, we conduct psychometric evaluations to assess its effectiveness and employ human evaluators to examine the generated content across various psychological constructs, including depression, cognitive distortions, and personality traits. Results demonstrate that PsychoGAT serves as an effective assessment tool, achieving statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity. Moreover, human evaluations confirm PsychoGAT's enhancements in content coherence, interactivity, interest, immersion, and satisfaction.

In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.5 and GPT-4 1. Utilizing In-Context Instruction Learning (ICIL) and Chain of Thought (CoT) reasoning, our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI generated reports. This is further enhanced by a Regression model that aggregates sentence evaluation scores. Experimental results show that our "Detailed GPT-4 (5-shot)" model achieves a 0.48 score, outperforming the METEOR metric by 0.19, while our "Regressed GPT-4" model shows even greater alignment with expert evaluations, exceeding the best existing metric by a 0.35 margin. Moreover, the robustness of our explanations has been validated through a thorough iterative strategy. We plan to publicly release annotations from radiology experts, setting a new standard for accuracy in future assessments. This underscores the potential of our approach in enhancing the quality assessment of AI-driven medical reports.

Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein Data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://github.com/zhangruochi/PepHarmony or http://www.healthinformaticslab.org/supp/., Comment: 25 pages, 5 figures, 3 tables

The emergence of online recruitment services has revolutionized the traditional landscape of job seeking and recruitment, necessitating the development of high-quality industrial applications to improve person-job fitting. Existing methods generally rely on modeling the latent semantics of resumes and job descriptions and learning a matching function between them. Inspired by the powerful role-playing capabilities of Large Language Models (LLMs), we propose to introduce a mock interview process between LLM-played interviewers and candidates. The mock interview conversations can provide additional evidence for candidate evaluation, thereby augmenting traditional person-job fitting based solely on resumes and job descriptions. However, characterizing these two roles in online recruitment still presents several challenges, such as developing the skills to raise interview questions, formulating appropriate answers, and evaluating two-sided fitness. To this end, we propose MockLLM, a novel applicable framework that divides the person-job matching process into two modules: mock interview generation and two-sided evaluation in handshake protocol, jointly enhancing their performance through collaborative behaviors between interviewers and candidates. We design a role-playing framework as a multi-role and multi-behavior paradigm to enable a single LLM agent to effectively behave with multiple functions for both parties. Moreover, we propose reflection memory generation and dynamic prompt modification techniques to refine the behaviors of both sides, enabling continuous optimization of the augmented additional evidence. Extensive experimental results show that MockLLM can achieve the best performance on person-job matching accompanied by high mock interview quality, envisioning its emerging application in real online recruitment in the future.

With the widespread application of artificial intelligence (AI), particularly deep learning (DL) and vision-based large language models (VLLMs), in skin disease diagnosis, the need for interpretability becomes crucial. However, existing dermatology datasets are limited in their inclusion of concept-level meta-labels, and none offer rich medical descriptions in natural language. This deficiency impedes the advancement of LLM-based methods in dermatological diagnosis. To address this gap and provide a meticulously annotated dermatology dataset with comprehensive natural language descriptions, we introduce SkinCAP: a multi-modal dermatology dataset annotated with rich medical captions. SkinCAP comprises 4,000 images sourced from the Fitzpatrick 17k skin disease dataset and the Diverse Dermatology Images dataset, annotated by board-certified dermatologists to provide extensive medical descriptions and captions. Notably, SkinCAP represents the world's first such dataset and is publicly available at https://huggingface.co/datasets/joshuachou/SkinCAP.

Noisy labels significantly hinder the accuracy and generalization of machine learning models, particularly due to ambiguous instance features. Traditional techniques that attempt to correct noisy labels directly, such as those using transition matrices, often fail to address the inherent complexities of the problem sufficiently. In this paper, we introduce EchoAlign, a transformative paradigm shift in learning from noisy labels. Instead of focusing on label correction, EchoAlign treats noisy labels ($\tilde{Y}$) as accurate and modifies corresponding instance features ($X$) to achieve better alignment with $\tilde{Y}$. EchoAlign's core components are (1) EchoMod: Employing controllable generative models, EchoMod precisely modifies instances while maintaining their intrinsic characteristics and ensuring alignment with the noisy labels. (2) EchoSelect: Instance modification inevitably introduces distribution shifts between training and test sets. EchoSelect maintains a significant portion of clean original instances to mitigate these shifts. It leverages the distinct feature similarity distributions between original and modified instances as a robust tool for accurate sample selection. This integrated approach yields remarkable results. In environments with 30% instance-dependent noise, even at 99% selection accuracy, EchoSelect retains nearly twice the number of samples compared to the previous best method. Notably, on three datasets, EchoAlign surpasses previous state-of-the-art techniques with a substantial improvement., Comment: 10 pages

Recent studies indicate that large multimodal models (LMMs) are highly robust against natural distribution shifts, often surpassing previous baselines. Despite this, domain-specific adaptation is still necessary, particularly in specialized areas like healthcare. Due to the impracticality of fine-tuning LMMs given their vast parameter space, this work investigates in-context learning (ICL) as an effective alternative for enhancing LMMs' adaptability. We find that the success of ICL heavily relies on the choice of demonstration, mirroring challenges seen in large language models but introducing unique complexities for LMMs facing distribution shifts. Our study addresses this by evaluating an unsupervised ICL method, TopKNearestPR, which selects in-context examples through a nearest example search based on feature similarity. We uncover that its effectiveness is limited by the deficiencies of pre-trained vision encoders under distribution shift scenarios. To address these challenges, we propose InvariantSelectPR, a novel method leveraging Class-conditioned Contrastive Invariance (CCI) for more robust demonstration selection. Specifically, CCI enhances pre-trained vision encoders by improving their discriminative capabilities across different classes and ensuring invariance to domain-specific variations. This enhancement allows the encoders to effectively identify and retrieve the most informative examples, which are then used to guide LMMs in adapting to new query samples under varying distributions. Our experiments show that InvariantSelectPR substantially improves the adaptability of LMMs, achieving significant performance gains on benchmark datasets, with a 34.2%$\uparrow$ accuracy increase in 7-shot on Camelyon17 and 16.9%$\uparrow$ increase in 7-shot on HAM10000 compared to the baseline zero-shot performance., Comment: 17 pages, 7 figures, 7 tables

In the typical urban intersection scenario, both vehicles and infrastructures are equipped with visual and LiDAR sensors. By successfully integrating the data from vehicle-side and road monitoring devices, a more comprehensive and accurate environmental perception and information acquisition can be achieved. The Calibration of sensors, as an essential component of autonomous driving technology, has consistently drawn significant attention. Particularly in scenarios involving multiple sensors collaboratively perceiving and addressing localization challenges, the requirement for inter-sensor calibration becomes crucial. Recent years have witnessed the emergence of the concept of multi-end cooperation, where infrastructure captures and transmits surrounding environment information to vehicles, bolstering their perception capabilities while mitigating costs. However, this also poses technical complexities, underscoring the pressing need for diverse end calibration. Camera and LiDAR, the bedrock sensors in autonomous driving, exhibit expansive applicability. This paper comprehensively examines and analyzes the calibration of multi-end camera-LiDAR setups from vehicle, roadside, and vehicle-road cooperation perspectives, outlining their relevant applications and profound significance. Concluding with a summary, we present our future-oriented ideas and hypotheses.

Pulmonary artery-vein segmentation is crucial for diagnosing pulmonary diseases and surgical planning, and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However, concerns regarding adverse health effects from contrast agents used in CTPA have constrained its clinical utility. In contrast, identifying arteries and veins using non-contrast CT, a conventional and low-cost clinical examination routine, has long been considered impossible. Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on both non-contrast CT and CTPA across various spatial resolutions. HiPaS first performs spatial normalization on raw CT scans via a super-resolution module, and then iteratively achieves segmentation results at different branch levels by utilizing the low-level vessel segmentation as a prior for high-level vessel segmentation. We trained and validated HiPaS on our established multi-centric dataset comprising 1,073 CT volumes with meticulous manual annotation. Both quantitative experiments and clinical evaluation demonstrated the superior performance of HiPaS, achieving a dice score of 91.8% and a sensitivity of 98.0%. Further experiments demonstrated the non-inferiority of HiPaS segmentation on non-contrast CT compared to segmentation on CTPA. Employing HiPaS, we have conducted an anatomical study of pulmonary vasculature on 10,613 participants in China (five sites), discovering a new association between pulmonary vessel abundance and sex and age: vessel abundance is significantly higher in females than in males, and slightly decreases with age, under the controlling of lung volumes (p < 0.0001). HiPaS realizing accurate artery-vein segmentation delineates a promising avenue for clinical diagnosis and understanding pulmonary physiology in a non-invasive manner.

Information-seeking dialogue systems are widely used in e-commerce systems, with answers that must be tailored to fit the specific settings of the online system. Given the user query, the information-seeking dialogue systems first retrieve a subset of response candidates, then further select the best response from the candidate set through re-ranking. Current methods mainly retrieve response candidates based solely on the current query, however, incorporating similar questions could introduce more diverse content, potentially refining the representation and improving the matching process. Hence, in this paper, we proposed a Query-bag based Pseudo Relevance Feedback framework (QB-PRF), which constructs a query-bag with related queries to serve as pseudo signals to guide information-seeking conversations. Concretely, we first propose a Query-bag Selection module (QBS), which utilizes contrastive learning to train the selection of synonymous queries in an unsupervised manner by leveraging the representations learned from pre-trained VAE. Secondly, we come up with a Query-bag Fusion module (QBF) that fuses synonymous queries to enhance the semantic representation of the original query through multidimensional attention computation. We verify the effectiveness of the QB-PRF framework on two competitive pretrained backbone models, including BERT and GPT-2. Experimental results on two benchmark datasets show that our framework achieves superior performance over strong baselines.

With the increasingly powerful performances and enormous scales of Pretrained Language Models (PLMs), promoting parameter efficiency in fine-tuning has become a crucial need for effective and efficient adaptation to various downstream tasks. One representative line of fine-tuning methods is Orthogonal Fine-tuning (OFT), which rigorously preserves the angular distances within the parameter space to preserve the pretrained knowledge. Despite the empirical effectiveness, OFT still suffers low parameter efficiency at $\mathcal{O}(d^2)$ and limited capability of downstream adaptation. Inspired by Givens rotation, in this paper, we proposed quasi-Givens Orthogonal Fine-Tuning (qGOFT) to address the problems. We first use $\mathcal{O}(d)$ Givens rotations to accomplish arbitrary orthogonal transformation in $SO(d)$ with provable equivalence, reducing parameter complexity from $\mathcal{O}(d^2)$ to $\mathcal{O}(d)$. Then we introduce flexible norm and relative angular adjustments under soft orthogonality regularization to enhance the adaptation capability of downstream semantic deviations. Extensive experiments on various tasks and PLMs validate the effectiveness of our methods.

Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing methods do not consider the trustworthiness of FCIL, i.e., improving continual utility, privacy, and efficiency simultaneously, which is greatly influenced by catastrophic forgetting and data heterogeneity among clients. To address this issue, we propose FedProK (Federated Prototypical Feature Knowledge Transfer), leveraging prototypical feature as a novel representation of knowledge to perform spatial-temporal knowledge transfer. Specifically, FedProK consists of two components: (1) feature translation procedure on the client side by temporal knowledge transfer from the learned classes and (2) prototypical knowledge fusion on the server side by spatial knowledge transfer among clients. Extensive experiments conducted in both synchronous and asynchronous settings demonstrate that our FedProK outperforms the other state-of-the-art methods in three perspectives of trustworthiness, validating its effectiveness in selectively transferring spatial-temporal knowledge.

Background: With the rising global prevalence of fatty liver disease related to metabolic dysfunction, the association of this common liver condition with chronic kidney disease (CKD) has become increasingly evident. In 2020, the more inclusive term metabolic dysfunction-associated fatty liver disease (MAFLD) was proposed to replace the term non-alcoholic fatty liver disease (NAFLD). The observed association between MAFLD and CKD and our understanding that CKD can be a consequence of underlying metabolic dysfunction support the notion that individuals with MAFLD are at higher risk of having and developing CKD compared with those without MAFLD. However, to date, there is no appropriate guidance on CKD in individuals with MAFLD. Furthermore, there has been little attention paid to the link between MAFLD and CKD in the Nephrology community. Methods and Results: Using a Delphi-based approach, a multidisciplinary panel of 50 international experts from 26 countries reached a consensus on some of the open research questions regarding the link between MAFLD and CKD. Conclusions: This Delphi-based consensus statement provided guidance on the epidemiology, mechanisms, management and treatment of MAFLD and CKD, as well as the relationship between the severity of MAFLD and risk of CKD, which establish a framework for the early prevention and management of these two common and interconnected diseases., NA

Steelmaking contributes 8% to the total CO2 emissions globally, primarily due to coal-based iron ore reduction. Clean hydrogen-based ironmaking has variable performance because the dominant gas-solid reduction mechanism is set by the defects and pores inside the mm- to nm-sized oxide particles that change significantly as the reaction progresses. While these governing dynamics are essential to establish continuous flow of iron and its ores through reactors, the direct link between agglomeration and chemistry is still contested due to missing measurements. In this work, we directly measure the connection between chemistry and agglomeration in the smallest iron oxides relevant to magnetite ores. Using synthesized spherical 10-nm magnetite particles reacting in H2, we resolve the formation and consumption of wüstite (Fe1-xO)-the step most commonly attributed to whiskering. Using X-ray diffraction, we resolve crystallographic anisotropy in the rate of the initial reaction. Complementary imaging demonstrated how the particles self-assemble, subsequently react, and grow into elongated whisker structures. Our insights into how morphologically uniform iron oxide particles react and agglomerate in H2 reduction enable future size-dependent models to effectively describe the multiscale aspects of iron ore reduction.

Breast cancer (BC) is a complex disease with a high degree of heterogeneity. The heterogeneity of BC could be detected at different biological levels using a variety of modern molecular biological techniques. These biotechniques could generate high-throughput and quantitative measurements, such as gene expression, copy number variation (CNV), DNA methylation, proteomics measurements, and so on. Meanwhile, the tumor morphology information obtained from medical images is also worthy of consideration in evaluating the heterogeneity of BC. Many machine-learning algorithms have been developed to help us to explore the heterogeneity of cancer from the abovementioned high-dimensional measurements. However, there are several challenges for characterizing BC heterogeneity based on the multi-modal biodata using the existing computational data analysis techniques. The first challenge is how to effectively combine the multi-modal biodata and find comprehensive and interpretable representations from them. Another challenge is how to address the execution infeasibility caused by the unpaired data problem (the publicly available datasets have unmatched multi-omics, medical images, and clinical outcome data). Besides, the model interpretability and privacy issues should also be carefully taken into consideration in machine learning-based BC research. This thesis aims to explore BC heterogeneity using thriving machine-learning algorithms at different data resolutions ranging from single genomics, multi-genomics, to proteomics and radiogenomics. We have four major objectives: 1)human epidermal growth factor receptor2 positive/estrogen receptor-positive (HER2+/ER+) BC stratification and prognostic gene signature identification using single genomic data; 2) BC subtyping using multiple genomics data; 3) Graph neural network (GNN) for BC hierarchical biological system mapping using graph-structured proteomics data; 4) BC prognostic radiogenomic biomarker identification. This thesis demon

Jiawei Qian,1,2 Xiulan Shen,3 Xin Gao,3 Qiuhua Sun2 1Department of Nursing, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 2School of Nursing, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 3Emergency and Trauma Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of ChinaCorrespondence: Qiuhua Sun, Email sqh807@163.comBackground: Delirium is common in older patients during the postoperative period, increasing the number of adverse events, and grip strength is associated with delirium. Subsyndromal delirium (SSD) is a prostate of clinical delirium; nevertheless, the relationship between grip strength and SSD has not been elucidated. This study aimed to examine the association between grip strength and SSD in the elderly after arthroplasty.Methods: A total of 181 patients were recruited from two Chinese hospitals. SSD and delirium were evaluated before the surgery and the first week after surgery using the confusion assessment method. The Mini-mental State Examination was used to assess patients’ cognitive function, and their grip strength was evaluated with an electronic hand dynamometer before surgery. Logistic regression and ROC curve analysis were conducted to determine the odds ratio and predictive value of grip strength for SSD.Results: The incidence of SSD and postoperative delirium (POD) was 41.44% and 14.36% for the elderly following arthroplasty respectively, and approximately 1/3 of SSD progressed into POD. Older age, declined cognitive function, fall history, and lower grip strength were risk factors for SSD (P< 0.05). The area under the ROC curve of grip strength was 0.863 and 0.900 for males and females respectively, and the cut-off point of it was determined to be 22.050 kg for men and 18.050 kg for women.Conclusion: SSD and POD are common among older people. Decreased grip strength, advanced age, lo

Zhou Zhu,1,2,&ast; Na Zhou,1,&ast; Shuangni Yu,3 Xin Gao,4 Xin Cheng,5 Yingyi Wang,1 Chunmei Bai1 1Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 2 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China; 3The Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 4The Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 5The Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yingyi Wang, Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People’s Republic of China, Email wangyingyi@pumch.cnBackground: Co-occurrence of breast cancer and non-Hodgkin’s lymphoma is a rare condition with diagnostic and therapeutic challenges. The coexistence of follicular lymphoma (FL) and triple-negative breast cancer (TNBC) has not been described previously.Case Presentation: A 46-year-old woman, already suffering a history of untreated, advanced-stage, high tumor burden FL, was admitted for a rapidly progressing right breast mass. Ultrasonography showed an 8.3 × 3.6 × 4.1 cm fungating mass in the right breast with enlarged lymph nodes (LNs) in bilateral axillae. PET-CT demonstrated increased 18F- FDG activity in right breast mass, LNs on both sides of the diaphragm, enlarged spleen, and bone marrow. Biopsy of the right breast mass

Translocation is one essential step for the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) to exert viral replication and transcription. Although cryo-EM structures of SARS-CoV-2 RdRp are available, the molecular mechanisms of dynamic translocation remain elusive. Herein, we constructed a Markov state model based on extensive molecular dynamics simulations to elucidate the translocation dynamics of the SARS-CoV-2 RdRp. We identified two intermediates that pinpoint the rate-limiting step of translocation and characterize the asynchronous movement of the template-primer duplex. The 3 '-terminal nucleotide in the primer strand lags behind due to the uneven distribution of protein-RNA interactions, while the translocation of the template strand is delayed by the hurdle residue K500. Even so, the two strands share the same "ratchet" to stabilize the polymerase in the post-translocation state, suggesting a Brownian-ratchet model. Overall, our study provides intriguing insights into SARS-CoV-2 replication and transcription, which would open a new avenue for drug discoveries.

Zi-Wei Yu,1,&ast; Sheng-Dan Pu,2,&ast; Xiao-Tong Sun,2 Xi-Chang Wang,1 Xin-Yuan Gao,2 Zhong-Yan Shan1 1Department of Endocrinology and Metabolism and the Institute of Endocrinology, The NHC Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, First Hospital of China Medical University, Shenyang, 110001, People’s Republic of China; 2Department of Endocrinology, First Affiliated Hospital of Harbin Medical University, Harbin, 150001, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhong-Yan Shan; Xin-Yuan Gao, Tel +8613804905018 ; +8613904517001, Email cmushanzhongyan@163.com; m15124768260@163.comPurpose: The prevalence of mild cognitive impairment (MCI) in patients with type 2 diabetes (T2D) is rapidly increasing. Thyroid hormones are key regulators of cognitive function in adults. The purpose of this study was to investigate the relationship between thyroid hormone sensitivity and MCI in euthyroid T2D patients.Patients and Methods: A total of 400 euthyroid T2D patients were enrolled in this cross-sectional study, including 218 patients with normal cognition and 182 MCI patients. The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive function. The free triiodothyronine to free thyroxine (FT3/FT4) ratio was calculated as a measure of peripheral sensitivity to thyroid hormones; the thyroid-stimulating hormone index (TSHI), thyrotrophic thyroxine resistance index (TT4RI) and thyroid feedback quantile-based index (TFQI) were calculated as measures of central sensitivity to thyroid hormones. Linear regression analysis and logistic regression analysis were performed to explore the relationships between these indices of thyroid hormone sensitivity and the MoCA score and MCI, respectively.Results: Compared with the normal cognitive function group, patients in the MCI group had higher TSHI, TT4RI and TFQI but a lower FT3/FT4 ratio (P< 0.05). The MoCA score was positively correlated with

Zi-Wei Yu,1 Sheng-Dan Pu,2 Xiao-Tong Sun,2 Xi-Chang Wang,1 Xin-Yuan Gao,2 Zhong-Yan Shan1 1Department of Endocrinology and Metabolism and the Institute of Endocrinology, The NHC Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, First Hospital of China Medical University, Shenyang, 110001, People’s Republic of China; 2Department of Endocrinology, First Affiliated Hospital of Harbin Medical University, Harbin, 150001, People’s Republic of ChinaCorrespondence: Zhong-Yan Shan, Department of Endocrinology and Metabolism and the Institute of Endocrinology, The NHC Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, First Hospital of China Medical University, Shenyang, 110001, People’s Republic of China, Tel +8613804905018, Email cmushanzhongyan@163.com Xin-Yuan Gao, Department of Endocrinology, First Affiliated Hospital of Harbin Medical University, Harbin, 150001, People’s Republic of China, Tel +8613904517001 ; +86 451 8555 5060, Email m15124768260@163.com

Thyroid nodules (TN) are localized morphological changes in the thyroid gland and can be benign or malignant.Objective: The present study investigates the relationships between biochemical markers in serum (s) and their homologs in washout (w) after fine-needle aspiration biopsy (FNAB) of the TN of interest and their correlation with cytology specimen findings.We investigated the relationships between serum biochemical markers nitric oxide (NO), thyroglobulin (TG), and calcitonin (CT), their homologs in washout after FNAB of the TN of interest, and cytology findings of biopsy samples classified according to the Bethesda system for thyroid cytopathology in this study, which included 86 subjects.Results: Washout TG (TGw) level positively correlates with the cytology finding of the biopsy. A higher level of TGw correlates with higher categories of the Bethesda classification and indicates a higher malignant potential. The levels of serum NO (NOs), serum TG (TGs), serum CT (CTs), and washout CT (CTw) do not correlate with the cytology finding of the biopsy, and the higher levels of washout NO (NOw) correspond to the more suspicious ultrasound findings.The findings of our study suggest that TGw and NOw could be used as potential predictors of malignancy in TN.

After the metabolic syndrome and its components, thyroid disorders represent the most common endocrine disorders, with increasing prevalence in the last two decades. Thyroid dysfunctions are distinguished by hyperthyroidism, hypothyroidism, or inflammation (thyroiditis) of the thyroid gland, in addition to the presence of thyroid nodules that can be benign or malignant. Thyroid cancer is typically detected via an ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) and cytological examination of the specimen. This approach has significant limitations due to the small sample size and inability to characterize follicular lesions adequately. Due to the rapid advancement of high-throughput molecular biology techniques, it is now possible to identify new biomarkers for thyroid neoplasms that can supplement traditional imaging modalities in postoperative surveillance and aid in the preoperative cytology examination of indeterminate or follicular lesions. Here, we review current knowledge regarding biomarkers that have been reliable in detecting thyroid neoplasms, making them valuable tools for assessing the efficacy of surgical procedures or adjunctive treatment after surgery. We are particularly interested in providing an up-to-date and systematic review of emerging biomarkers, such as mRNA and non-coding RNAs, that can potentially detect thyroid neoplasms in clinical settings. We discuss evidence for miRNA, lncRNA and circRNA dysregulation in several thyroid neoplasms and assess their potential for use as diagnostic and prognostic biomarkers.

Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved $\sim$5\% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.

Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients' data are being collected and used for model training, especially in intelligent healthcare, thus making intelligent healthcare a sector where technology must meet the law, regulations, and privacy principles to ensure that the innovation is for the common good. In order to secure patients' right to be forgotten, we proposed a novel solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing a new approach called knowledge purification. To implement our solution, we developed AFS, a unified open-source software, which is able to evaluate and revoke patients' private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets with various data sizes and architectures of deep learning networks. The software is publicly available at \url{https://github.com/JoshuaChou2018/AFS}.

Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential totackle this challenge in an efficient manner. Inspired by this, in this work we propose a multi-agent RL framework for simultaneous multi-target landmark detection. This framework is aimed to learn from incomplete or (and) complete images to form an implicit knowledge of global structure, which is consolidated during the training stage for the detection of targets from either complete or incomplete test images. To further explicitly exploit the global structural information from incomplete images, we propose to embed a shape model into the RL process. With this prior knowledge, the proposed RL model can not only localize dozens of targetssimultaneously, but also work effectively and robustly in the presence of incomplete images. We validated the applicability and efficacy of the proposed method on various multi-target detection tasks with incomplete images from practical clinics, using body dual-energy X-ray absorptiometry (DXA), cardiac MRI and head CT datasets. Results showed that our method could predict whole set of landmarks with incomplete training images up to 80% missing proportion (average distance error 2.29 cm on body DXA), and could detect unseen landmarks in regions with missing image information outside FOV of target images (average distance error 6.84 mm on 3D half-head CT)., Comment: 29 pages, 13 figures

Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug-related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we're robust against unseen drugs and surpass almost 15% AU ROC compared to the second-best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations., Comment: 30 pages, 12 figures, accepted by TOIS 2022

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer’s disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs’ potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease’s comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods.

Cardiovascular (CV) disorders are steadily increasing, making them the world’s most prevalent health issue. New research highlights the importance of insulin-like growth factor 1 (IGF-1) for maintaining CV healthMethodsWe searched PubMed and MEDLINE for English and non-English articles with English abstracts published between 1957 (when the first report on IGF-1 identification was published) and 2022. The top search terms were: IGF-1, cardiovascular disease, IGF-1 receptors, IGF-1 and microRNAs, therapeutic interventions with IGF-1, IGF-1 and diabetes, IGF-1 and cardiovascular disease. The search retrieved original peer-reviewed articles, which were further analyzed, focusing on the role of IGF-1 in pathophysiological conditions. We specifically focused on including the most recent findings published in the past five years.ResultsIGF-1, an anabolic growth factor, regulates cell division, proliferation, and survival. In addition to its well-known growth-promoting and metabolic effects, there is mounting evidence that IGF-1 plays a specialized role in the complex activities that underpin CV function. IGF-1 promotes cardiac development and improves cardiac output, stroke volume, contractility, and ejection fraction. Furthermore, IGF-1 mediates many growth hormones (GH) actions. IGF-1 stimulates contractility and tissue remodeling in humans to improve heart function after myocardial infarction. IGF-1 also improves the lipid profile, lowers insulin levels, increases insulin sensitivity, and promotes glucose metabolism. These findings point to the intriguing medicinal potential of IGF-1. Human studies associate low serum levels of free or total IGF-1 with an increased risk of CV and cerebrovascular illness. Extensive human trials are being conducted to investigate the therapeutic efficacy and outcomes of IGF-1-related therapy.DiscussionWe anticipate the development of novel IGF-1-related therapy with minimal side effects. This review discusses recent findings on the role

Diabetes mellitus (DM) is on the rise, necessitating the development of novel therapeutic and preventive strategies to mitigate the disease’s debilitating effects. Diabetic cardiomyopathy (DCMP) is among the leading causes of morbidity and mortality in diabetic patients globally. DCMP manifests as cardiomyocyte hypertrophy, apoptosis, and myocardial interstitial fibrosis before progressing to heart failure. Evidence suggests that non-coding RNAs, such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), regulate diabetic cardiomyopathy-related processes such as insulin resistance, cardiomyocyte apoptosis and inflammation, emphasizing their heart-protective effects. This paper reviewed the literature data from animal and human studies on the non-trivial roles of miRNAs and lncRNAs in the context of DCMP in diabetes and demonstrated their future potential in DCMP treatment in diabetic patients.

An imbalance between pro-oxidative and antioxidative cellular mechanisms is oxidative stress (OxS) which may be systemic or organ-specific. Although OxS is a consequence of normal body and organ physiology, severely impaired oxidative homeostasis results in DNA hydroxylation, protein denaturation, lipid peroxidation, and apoptosis, ultimately compromising cells’ function and viability. The thyroid gland is an organ that exhibits both oxidative and antioxidative processes. In terms of OxS severity, the thyroid gland’s response could be physiological (i.e. hormone production and secretion) or pathological (i.e. development of diseases, such as goitre, thyroid cancer, or thyroiditis). Protective nutritional antioxidants may benefit defensive antioxidative systems in resolving pro-oxidative dominance and redox imbalance, preventing or delaying chronic thyroid diseases. This review provides information on nutritional antioxidants and their protective roles against impaired redox homeostasis in various thyroid pathologies. We also review novel findings related to the connection between the thyroid gland and gut microbiome and analyze the effects of probiotics with antioxidant properties on thyroid diseases. Copyright © 2023 Macvanin, Gluvic, Zafirovic, Gao, Essack and Isenovic.

Ma D, Gao X, Dan Y, et al. Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers. Onco Targets Ther. 2020;13:6927–6935. Recently, the authors research group reviewed their published article and found that there was an error in Figure 3B on page 6932, which was mistakenly duplicated in Figure 4B when they prepared the revised high-resolution Figure 3. Corrected Figure 3 as follows: Figure 3 Results of classifier 1. (A) The receiver-operating characteristic curve of classifier 1. (B) Selection of optimal features using ANOVA. (C) Contribution of selected radiomics features of classifier 1. The authors apologize and advise this error does not influence the results or the conclusion.

Yi-Tong Zhang,&ast; Hui-Yao Li,&ast; Xiao-Tong Sun, Xue-Wei Tong, Yong-Yan Shan, Yu-Xin Xu, Sheng-Dan Pu, Xin-Yuan Gao Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xin-Yuan Gao, Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University, 23 Youzheng Street, Harbin, 150001, People’s Republic of China, Email 15124768260@163.comBackground: Diabetic retinopathy (DR) is strongly associated with cardiovascular disease, which is a risk factor for sudden cardiac death (SCD). The index of cardiac electrophysiological balance (iCEB) and the frontal QRS-T angle are recommended to predict the risk of ventricular arrhythmias more than other ECG parameters. However, the relationships between these two markers and DR have not yet been explored. The aim of this study was to investigate the variation in the iCEB, corrected iCEB (iCEBc) and frontal QRS-T angle in different stages of DR and determine whether there are associations between these markers and DR.Methods: The sample comprised 665 patients with Type 2 diabetes mellitus (T2DM) who were classified into three groups: no DR (NDR), mild to moderate non-proliferative DR (NPDR), and vision-threatening DR (VTDR). Twelve-lead ECG was performed and the QT, QTc, QRS duration, iCEB, iCEBc and frontal QRS-T angle were recorded and compared across the groups.Results: The VTDR group had a significantly higher iCEBc and frontal QRS-T angle than the NDR and NPDR groups. After controlling for confounding variables, the correlations between the iCEBc (OR=2.217, 95% CI=1.464– 3.358, P< 0.001), frontal QRS-T angle (OR=1.017, 95% CI=1.008– 1.025, P< 0.001) and DR risk remained (P< 0.05). Subjects in the fourth iCEBc quartile (adjusted OR=2.612, 95% CI=1.411– 4.834, p=0.002) had a much higher chance of developing DR compared to those in