Epidemiology, microbiology and antibiotic treatment of bacterial and fungal meningitis among very preterm infants in China: a cross-sectional study.

OBJECTIVE: Neonatal meningitis significantly contributes to neonatal morbidity and mortality, yet large-scale epidemiological data in developing countries, particularly among very preterm infants (VPIs), remain sparse. This study aimed to describe the epidemiology of meningitis among VPIs in China. DESIGN: Cross-sectional study using the Chinese Neonatal Network database from 2019 to 2021. SETTING: 79 tertiary neonatal intensive care units in China. PATIENTS: Infants with gestational age <32 weeks or birth weight <1500 g. MAIN OUTCOME MEASURES: Incidence, pathogen distribution, antimicrobial use and outcomes of bacterial and fungal meningitis. RESULTS: Of 31 915 VPIs admitted, 122 (0.38%) infants were diagnosed with culture-confirmed meningitis, with 14 (11.5%) being early-onset (≤6 days of age) and 108 (88.5%) being late-onset (>6 days of age). The overall in-hospital mortality was 18.0% (22/122). A total of 127 pathogens were identified, among which 63.8% (81/127) were Gram-negative bacteria, 24.4% (31/127) were Gram-positive bacteria and 11.8% (15/127) were fungi. In terms of empirical therapy (on the day of the first lumbar puncture), the most commonly used antibiotic was meropenem (54.9%, 67/122). For definitive therapy (on the sixth day following the first lumbar puncture, 86 cases with available antibiotic data), meropenem (60.3%, 35/58) and vancomycin (57.1%, 16/28) were the most used antibiotics for Gram-negative and Gram-positive bacterial meningitis, respectively. 44% of infants with Gram-positive bacterial meningitis and 52% with Gram-negative bacterial meningitis received antibiotics for more than 3 weeks. CONCLUSION: 0.38% of VPIs in Chinese neonatal intensive care units were diagnosed with meningitis, experiencing significant mortality and inappropriate antibiotic therapy. Gram-negative bacteria were the predominant pathogens, with fungi emerging as a significant cause.

Effect of smart city construction on an urban transition to a low-carbon economy: evidence from China.

China is currently in a new era of an urban transition to a low-carbon economy and digital economic development. Smart cities, as an advanced form of information-based urban development, may be the key to the urban transition to low-carbon emissions. This paper examined the effect of smart city construction (SCC) on urban low-carbon transitions and its transmission mechanisms in China from the dual perspectives of reducing urban total carbon emissions (TCE) and improving urban total-factor carbon emission efficiency (TFCEE). Utilizing a multi-period difference in differences (DID) method, this study was conducted based on panel data of 245 Chinese prefecture-level cities from 2003 to 2021. The results demonstrated that SCC both reduced TCE and enhanced TFCEE. The effects of SCC were stronger in cities with more stringent environmental regulations. SCC achieved the dual effect of reducing TCE and enhancing urban TFCEE by promoting green technological progress and a low-carbon transformation of city residents' lifestyles. Moreover, optimization of the industrial structure was also a transmission mechanism for SCC to improve TFCEE. These conclusions provide an empirical basis for the SCC to empower low-carbon transitions of cities and help countries in different regions to transform the extensive urban development mode and promote urban low-carbon economic development.

Deciphering the impact of aggregated autophagy-related genes TUBA1B and HSP90AA1 on colorectal cancer evolution: a single-cell sequencing study of the tumor microenvironment.

BACKGROUND: Colorectal cancer (CRC) is the third most prevalent cancer worldwide, with the tumor microenvironment (TME) playing a crucial role in its progression. Aggregated autophagy (AA) has been recognized as a factor that exacerbates CRC progression. This study aims to study the relationship between aggregated autophagy and CRC using single-cell sequencing techniques. Our goal is to explain the heterogeneity of the TME and to explore the potential for targeted personalized therapies. OBJECTIVE: To study the role of AA in CRC, we employed single-cell sequencing to discern distinct subpopulations within the TME. These subpopulations were characterized by their autophagy levels and further analyzed to identify specific biological processes and marker genes. RESULTS: Our study revealed significant correlations between immune factors and both clinical and biological characteristics of the tumor microenvironment (TME), particularly in cells expressing TUBA1B and HSP90AA1. These immune factors were associated with T cell depletion, a reduction in protective factors, diminished efficacy of immune checkpoint blockade (ICB), and enhanced migration of cancer-associated fibroblasts (CAFs), resulting in pronounced inflammation. In vitro experiments showd that silencing TUBA1B and HSP90AA1 using siRNA (Si-TUBA1B and Si-HSP90AA1) significantly reduced the expression of IL-6, IL-7, CXCL1, and CXCL2 and inhibition of tumor cell growth in Caco-2 and Colo-205 cell lines. This reduction led to a substantial alleviation of chronic inflammation and highlighted the heterogeneous nature of the TME. CONCLUSION: This study marks an initial foray into understanding how AA-associated processes may potentiate the TME and weaken immune function. Our findings provide insights into the complex dynamics of the TME and highlight potential targets for therapeutic intervention, suggesting a key role for AA in the advancement of colorectal cancer.

Development of human papillomavirus and its detection methods (Review).

Human papillomavirus (HPV) infection plays an important role in cervical cancer. HPV is classified within the Papillomaviridae family and is a non-enveloped, small DNA virus. HPV infection can be classified into two distinct scenarios: i) With or without integration into the host chromosomes. Detection of its infection can be useful in the study of cervical lesions. In the present review, the structural and functional features of HPV, HPV typing, infection and transmission mode, the risk factors for cervical susceptibility to infection and HPV detection methods are described in detail. The development of HPV detection methods may have far-reaching significance in the prevention and treatment of cervical disease. This review summarizes the advantages and limitations of each HPV detection method.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Systemic aging fuels heart failure: Molecular mechanisms and therapeutic avenues.

Systemic aging influences various physiological processes and contributes to structural and functional decline in cardiac tissue. These alterations include an increased incidence of left ventricular hypertrophy, a decline in left ventricular diastolic function, left atrial dilation, atrial fibrillation, myocardial fibrosis and cardiac amyloidosis, elevating susceptibility to chronic heart failure (HF) in the elderly. Age-related cardiac dysfunction stems from prolonged exposure to genomic, epigenetic, oxidative, autophagic, inflammatory and regenerative stresses, along with the accumulation of senescent cells. Concurrently, age-related structural and functional changes in the vascular system, attributed to endothelial dysfunction, arterial stiffness, impaired angiogenesis, oxidative stress and inflammation, impose additional strain on the heart. Dysregulated mechanosignalling and impaired nitric oxide signalling play critical roles in the age-related vascular dysfunction associated with HF. Metabolic aging drives intricate shifts in glucose and lipid metabolism, leading to insulin resistance, mitochondrial dysfunction and lipid accumulation within cardiomyocytes. These alterations contribute to cardiac hypertrophy, fibrosis and impaired contractility, ultimately propelling HF. Systemic low-grade chronic inflammation, in conjunction with the senescence-associated secretory phenotype, aggravates cardiac dysfunction with age by promoting immune cell infiltration into the myocardium, fostering HF. This is further exacerbated by age-related comorbidities like coronary artery disease (CAD), atherosclerosis, hypertension, obesity, diabetes and chronic kidney disease (CKD). CAD and atherosclerosis induce myocardial ischaemia and adverse remodelling, while hypertension contributes to cardiac hypertrophy and fibrosis. Obesity-associated insulin resistance, inflammation and dyslipidaemia create a profibrotic cardiac environment, whereas diabetes-related metabolic disturbances further impair cardiac function. CKD-related fluid overload, electrolyte imbalances and uraemic toxins exacerbate HF through systemic inflammation and neurohormonal renin-angiotensin-aldosterone system (RAAS) activation. Recognizing aging as a modifiable process has opened avenues to target systemic aging in HF through both lifestyle interventions and therapeutics. Exercise, known for its antioxidant effects, can partly reverse pathological cardiac remodelling in the elderly by countering processes linked to age-related chronic HF, such as mitochondrial dysfunction, inflammation, senescence and declining cardiomyocyte regeneration. Dietary interventions such as plant-based and ketogenic diets, caloric restriction and macronutrient supplementation are instrumental in maintaining energy balance, reducing adiposity and addressing micronutrient and macronutrient imbalances associated with age-related HF. Therapeutic advancements targeting systemic aging in HF are underway. Key approaches include senomorphics and senolytics to limit senescence, antioxidants targeting mitochondrial stress, anti-inflammatory drugs like interleukin (IL)-1ß inhibitors, metabolic rejuvenators such as nicotinamide riboside, resveratrol and sirtuin (SIRT) activators and autophagy enhancers like metformin and sodium-glucose cotransporter 2 (SGLT2) inhibitors, all of which offer potential for preserving cardiac function and alleviating the age-related HF burden.

IL-17A exacerbates caspase-12-dependent neuronal apoptosis following ischemia through the Src-PLCγ-calpain pathway.

Interleukin-17 A (IL-17 A) contributes to inflammation and causes secondary injury in post-stroke patients. However, little is known regarding the mechanisms that IL-17 A is implicated in the processes of neuronal death during ischemia. In this study, the mouse models of middle cerebral artery occlusion/reperfusion (MCAO/R)-induced ischemic stroke and oxygen-glucose deprivation/reoxygenation (OGD/R)-simulated in vitro ischemia in neurons were employed to explore the role of IL-17 A in promoting neuronal apoptosis. Mechanistically, endoplasmic reticulum stress (ERS)-induced neuronal apoptosis was accelerated by IL-17 A activation through the caspase-12-dependent pathway. Blocking calpain or phospholipase Cγ (PLCγ) inhibited IL-17 A-mediated neuronal apoptosis under ERS by inhibiting caspase-12 cleavage. Src and IL-17 A are linked, and PLCγ directly binds to activated Src. This binding causes intracellular Ca2+ flux and activates the calpain-caspase-12 cascade in neurons. The neurological scores showed that intracerebroventricular (ICV) injection of an IL-17 A neutralizing mAb decreased the severity of I/R-induced brain injury and suppressed apoptosis in MCAO mice. Our findings reveal that IL-17 A increases caspase-12-mediated neuronal apoptosis, and IL-17 A suppression may have therapeutic potential for ischemic stroke.

Adducin Regulates Sarcomere Disassembly During Cardiomyocyte Mitosis.

BACKGROUND: Recent interest in understanding cardiomyocyte cell cycle has been driven by potential therapeutic applications in cardiomyopathy. However, despite recent advances, cardiomyocyte mitosis remains a poorly understood process. For example, it is unclear how sarcomeres are disassembled during mitosis to allow the abscission of daughter cardiomyocytes. METHODS: Here, we use a proteomics screen to identify adducin, an actin capping protein previously not studied in cardiomyocytes, as a regulator of sarcomere disassembly. We generated many adeno-associated viruses and cardiomyocyte-specific genetic gain-of-function models to examine the role of adducin in neonatal and adult cardiomyocytes in vitro and in vivo. RESULTS: We identify adducin as a regulator of sarcomere disassembly during mammalian cardiomyocyte mitosis. α/γ-adducins are selectively expressed in neonatal mitotic cardiomyocytes, and their levels decline precipitously thereafter. Cardiomyocyte-specific overexpression of various splice isoforms and phospho-isoforms of α-adducin in vitro and in vivo identified Thr445/Thr480 phosphorylation of a short isoform of α-adducin as a potent inducer of neonatal cardiomyocyte sarcomere disassembly. Concomitant overexpression of this α-adducin variant along with γ-adducin resulted in stabilization of the adducin complex and persistent sarcomere disassembly in adult mice, which is mediated by interaction with α-actinin. CONCLUSIONS: These results highlight an important mechanism for coordinating cytoskeletal morphological changes during cardiomyocyte mitosis.

Acetylation of ELMO1 correlates with Rac1 activity and colorectal cancer progress.

Acetylation, a critical regulator of diverse cellular processes, holds significant implications in various cancer contexts. Further understanding of the acetylation patterns of key cancer-driven proteins is crucial for advancing therapeutic strategies in cancer treatment. This study aimed to unravel the acetylation patterns of Engulfment and Cell Motility Protein 1 (ELMO1) and its relevance to the pathogenesis of colorectal cancer (CRC). Immunoprecipitation and mass spectrometry precisely identified lysine residue 505 (K505) as a central acetylation site in ELMO1. P300 emerged as the acetyltransferase for ELMO1 K505 acetylation, while SIRT2 was recognized as the deacetylase. Although K505 acetylation minimally affected ELMO1's localization and stability, it played a crucial role in mediating ELMO1-Dock180 interaction, thereby influencing Rac1 activation. Functionally, ELMO1 K505 acetylation proved to be a pivotal factor in CRC progression, exerting its influence on key cellular processes. Clinical analysis of CRC samples unveiled elevated ELMO1 acetylation in primary tumors, indicating a potential association with CRC pathologies. This work provides insights into ELMO1 acetylation and its significance in advancing potentially therapeutic interventions in CRC treatment.

Cardiac function evaluation in children with spinal muscular atrophy: A case-control study.

BACKGROUND: Spinal muscular atrophy (SMA) is an autosomal recessive disorder characterized by degeneration of lower motor neurons, resulting in progressive muscle weakness and atrophy. However, little is known regarding the cardiac function of children with SMA. METHODS: We recruited SMA patients younger than 18 years of age from January 1, 2022, to April 1, 2022, in the First Affiliated Hospital of Sun Yat-sen University. All patients underwent a comprehensive cardiac evaluation before treatment, including history taking, physical examination, blood tests of cardiac biomarkers, assessment of echocardiography and electrocardiogram. Age/gender-matched healthy volunteers were recruited as controls. RESULTS: A total of 36 SMA patients (26 with SMA type 2 and 10 with SMA type 3) and 40 controls were enrolled in the study. No patient was clinically diagnosed with heart failure. Blood tests showed elevated values of creatine kinase isoenzyme M and isoenzyme B (CK-MB) mass and high-sensitivity cardiac troponin T (hs-cTnT) in spinal muscular atrophy (SMA) patients. Regarding echocardiographic parameters, SMA children were detected with lower global left and right ventricular longitudinal strain, abnormal diastolic filling velocities of trans-mitral and trans-tricuspid flow. The results revealed no clinical heart dysfunction in SMA patients, but subclinical ventricular dysfunction was seen in SMA children including the diastolic function and myocardial performance. Some patients presented with elevated heart rate and abnormal echogenicity of aortic valve or wall. Among these SMA patients, seven patients (19.4%) had scoliosis. The Cobb's angles showed a significant negative correlation with LVEDd/BSA, but no correlation with other parameters, suggesting that mild scoliosis did not lead to significant cardiac dysfunction. CONCLUSIONS: Our findings warrant increased attention to the cardiac status and highlight the need to investigate cardiac interventions in SMA children.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Pathogen-specific alterations in intestinal microbiota precede urinary tract infections in preterm infants: a longitudinal case-control study.

Urinary tract infections (UTIs) are among the most common late-onset infections in preterm infants, characterized by nonspecific symptoms and a pathogenic spectrum that diverges from that of term infants and older children, which present unique diagnostic and therapeutic challenges. Existing data on the role of gut microbiota in UTI pathogenesis in this demographic are limited. This study aims to investigate alterations in gut microbiota and fecal calprotectin levels and their association with the development of UTIs in hospitalized preterm infants. A longitudinal case-control study was conducted involving preterm infants admitted between January 2018 and October 2020. Fecal samples were collected weekly and analyzed for microbial profiles and calprotectin levels. Propensity score matching, accounting for key perinatal factors including age and antibiotic use, was utilized to match samples from UTI-diagnosed infants to those from non-UTI counterparts. Among the 151 preterm infants studied, 53 were diagnosed with a UTI, predominantly caused by Enterobacteriaceae (79.3%) and Enterococcaceae (19.0%). Infants with UTIs showed a significantly higher abundance of these families compared to non-UTI infants, for both Gram-negative and positive pathogens, respectively. Notably, there was a significant pre-UTI increase in the abundance of pathogen-specific taxa in infants later diagnosed with UTIs, offering high predictive value for early detection. Shotgun metagenomic sequencing further confirmed the dominance of specific pathogenic species pre-UTI and revealed altered virulence factor profiles associated with Klebsiella aerogenes and Escherichia coli infections. Additionally, a decline in fecal calprotectin levels was observed preceding UTI onset, particularly in cases involving Enterobacteriaceae. The observed pathogen-specific alterations in the gut microbiota preceding UTI onset offer novel insight into the UTI pathogenesis and promising early biomarkers for UTIs in preterm infants, potentially enhancing the timely management of this common infection. However, further validation in larger cohorts is essential to confirm these findings.

Integrating Single-Cell and Spatial Transcriptomics Reveals Heterogeneity of Early Pig Skin Development and a Subpopulation with Hair Placode Formation.

The dermis and epidermis, crucial structural layers of the skin, encompass appendages, hair follicles (HFs), and intricate cellular heterogeneity. However, an integrated spatiotemporal transcriptomic atlas of embryonic skin has not yet been described and would be invaluable for studying skin-related diseases in humans. Here, single-cell and spatial transcriptomic analyses are performed on skin samples of normal and hairless fetal pigs across four developmental periods. The cross-species comparison of skin cells illustrated that the pig epidermis is more representative of the human epidermis than mice epidermis. Moreover, Phenome-wide association study analysis revealed that the conserved genes between pigs and humans are strongly associated with human skin-related diseases. In the epidermis, two lineage differentiation trajectories describe hair follicle (HF) morphogenesis and epidermal development. By comparing normal and hairless fetal pigs, it is found that the hair placode (Pc), the most characteristic initial structure in HFs, arises from progenitor-like OGN+/UCHL1+ cells. These progenitors appear earlier in development than the previously described early Pc cells and exhibit abnormal proliferation and migration during differentiation in hairless pigs. The study provides a valuable resource for in-depth insights into HF development, which may serve as a key reference atlas for studying human skin disease etiology using porcine models.

CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization.

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point clouds, which are limited to reconstructing closed surfaces. Some other methods tried to represent open surfaces using unsigned distance functions (UDF) which are learned from ground truth distances. However, the learned UDF is hard to provide smooth distance fields due to the discontinuous character of point clouds. In this paper, we propose CAP-UDF, a novel method to learn consistency-aware UDF from raw point clouds. We achieve this by learning to move queries onto the surface with a field consistency constraint, where we also enable to progressively estimate a more accurate surface. Specifically, we train a neural network to gradually infer the relationship between queries and the approximated surface by searching for the moving target of queries in a dynamic way. Meanwhile, we introduce a polygonization algorithm to extract surfaces using the gradients of the learned UDF. We conduct comprehensive experiments in surface reconstruction for point clouds, real scans or depth maps, and further explore our performance in unsupervised point normal estimation, which demonstrate non-trivial improvements of CAP-UDF over the state-of-the-art methods.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

[A case of trichoblastoma of the auricular cavity and review of the literature].

Trichoblastoma（TB） is a rare germ cell skin adnexal tumor of the hair, and it is a rare follicular tumor of the skin that differentiates from the hair germ epithelium and is often regarded as a benign skin tumorHowever, it is poorly confined and has a local infiltrative growth pattern. tb occurs in the head and neck region, especially in the face, and presents clinically as a slow growing, well-defined and elevated nodule. TB is routinely treated surgically. Due to the lack of universally accepted treatment guidelines or protocols, the recurrence rate after surgery is high, which makes clinical cure more difficult. In this study, a 65-year-old female patient was found to have a swelling with recurrent rupture and pus flow from the right external auditory canal opening and the auricular cavity. After initial misdiagnosis as otitis externa, she was treated with conventional anti-infective therapy, but her symptoms did not resolve and gradually worsened before coming to our hospital. The condition presented in this case is relativelyrare,therepre,timely and accurate diagnosis and treatment are crucial for prognosis improvement of such diseases.

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Association of 8-hydroxy-2'-deoxyguanosine with motoric cognitive risk in elderly Chinese people: RUGAO longevity and aging cross-sectional study.

BACKGROUND: Motor cognitive risk syndrome (MCR) represents a critical pre-dementia and disability state characterized by a combination of objectively measured slow walking speed and subjective memory complaints (SMCs). This study aims to identify risk factors for MCR and investigate the relationship between plasma levels of 8-hydroxy-2'-deoxyguanosine (8-OHdG) and MCR among Chinese community-dwelling elderly populations. METHODS: A total of 1312 participants were involved in this study based on the data of the Rugao Longevity and Aging Study (RuLAS). The MCR was characterized by SMCs and slow walking speed. The SCCs were defined as a positive answer to the question 'Do you feel you have more problems with memory than most?' in a 15-item Geriatric Depression Scale. Slow walking speed was determined by one standard deviation or more below the mean value of the patient's age and gender group. The plasma of 8-OHdG were measured by a technician in the biochemistry laboratory of the Rugao People's Hospital during the morning of the survey. RESULTS: The prevalence of MCR was found to be 7.9%. After adjusting for covariates, significant associations with MCR were observed in older age (OR 1.057; p = 0.018), history of cerebrovascular disease (OR 2.155; p = 0.010), and elevated 8-OHdG levels (OR 1.007; p = 0.003). CONCLUSIONS: This study indicated the elevated plasma 8-OHdG is significantly associated with increased MCR risk in the elderly, suggesting its potential as a biomarker for early detection and intervention in MCR. This finding underscores the importance of monitoring oxidative DNA damage markers in predicting cognitive and motor function declines, offering new avenues for research and preventive strategies in aging populations.