Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation.

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.

Clonal landscape of autoantibody-secreting plasmablasts in COVID-19 patients.

Whereas severe COVID-19 is often associated with elevated autoantibody titers, the underlying mechanism behind their generation has remained unclear. Here we report clonal composition and diversity of autoantibodies in humoral response to SARS-CoV-2. Immunoglobulin repertoire analysis and characterization of plasmablast-derived monoclonal antibodies uncovered clonal expansion of plasmablasts producing cardiolipin (CL)-reactive autoantibodies. Half of the expanded CL-reactive clones exhibited strong binding to SARS-CoV-2 antigens. One such clone, CoV1804, was reactive to both CL and viral nucleocapsid (N), and further showed anti-nucleolar activity in human cells. Notably, antibodies sharing genetic features with CoV1804 were identified in COVID-19 patient-derived immunoglobulins, thereby constituting a novel public antibody. These public autoantibodies had numerous mutations that unambiguously enhanced anti-N reactivity, when causing fluctuations in anti-CL reactivity along with the acquisition of additional self-reactivities, such as anti-nucleolar activity, in the progeny. Thus, potentially CL-reactive precursors may have developed multiple self-reactivities through clonal selection, expansion, and somatic hypermutation driven by viral antigens. Our results revealed the nature of autoantibody production during COVID-19 and provided novel insights into the origin of virus-induced autoantibodies.

Full-length nanopore sequencing of circular RNA landscape in peripheral blood cells following sequential BNT162b2 mRNA vaccination.

Circular RNAs (circRNA) lack 5' or 3' ends; their unique covalently closed structures prevent RNA degradation by exonucleases. These characteristics provide circRNAs with high pharmaceutical stability and biostability relative to current standard-of-care linear mRNAs. CircRNA levels are reportedly associated with certain human diseases, making them novel disease biomarkers and a noncanonical class of therapeutic targets. In this study, the endogenous circRNAs underlying the response to BNT162b2 mRNA vaccination were evaluated. To this end, peripheral blood samples were subjected to full-length sequencing of circRNAs via nanopore sequencing and transcriptome sequencing. Fifteen samples, comprising pre-, first, and second vaccination cohorts, were obtained from five healthcare workers with no history of SARS-CoV-2 infection or previous vaccination. A total of 4706 circRNAs were detected; following full-length sequencing, 4217 novel circRNAs were identified as being specifically expressed during vaccination. These circRNAs were enriched in the binding motifs of stress granule assemblies and SARS-CoV-2 RNA binding proteins, namely poly(A) binding protein cytoplasmic 1 (PABPC1), pumilio RNA binding family member 1 (PUM1), and Y box binding protein 1 (YBX1). Moreover, 489 circRNAs were identified as previously reported miRNA sponges. The differentially expressed circRNAs putatively originated from plasma B cells compared to circRNAs reported in human blood single-cell RNA sequencing datasets. The pre- and post-vaccination differences observed in the circRNA expression landscape in response to the SARS-CoV-2 BNT162b2 mRNA vaccine.

VesiMCNN: Using pre-trained protein language models and multiple window scanning convolutional neural networks to identify vesicular transport proteins.

Vesicular transport is a critical cellular process responsible for the proper organization and functioning of eukaryotic cells. This mechanism relies on specialized vesicles that shuttle macromolecules, such as proteins, across the cellular landscape, a process pivotal to maintaining cellular homeostasis. Disruptions in vesicular transport have been linked to various disease mechanisms, including cancer and neurodegenerative disorders. In this study, we present vesiMCNN, a novel computational approach that integrates pre-trained protein language models with a multi-window scanning convolutional neural network architecture to accurately identify vesicular transport proteins. To the best of our knowledge, this is the first study to leverage the power of pre-trained language models in combination with the multi-window scanning technique for this task. Our method achieved a Matthews Correlation Coefficient (MCC) of 0.558 and an Area Under the Receiver Operating Characteristic (AUC-ROC) of 0.933, outperforming existing state-of-the-art approaches. Additionally, we have curated a comprehensive benchmark dataset for the study of vesicular transport proteins, which can facilitate further research in this field. The remarkable performance of our model, combined with the comprehensive dataset and novel deep learning model, marks a significant advancement in the field of vesicular transport protein research.

Microbial community response to temperature reduction during anaerobic treatment of long chain fatty acids-containing wastewater.

Acclimating mesophilic biomass to low temperatures have been used to start-up psychrophilic anaerobic reactors, but limited microbial information is available during the acclimation. To investigate microbial responses to temperature reductions, duplicate lab-scale anaerobic digestion (AD) reactors were operated for 166 days, with the temperature being reduced from 37°C to 15°C, using synthetic long chain fatty acid (LCFA)-containing wastewater as the feedstock. The acclimated biomass at 15°C exhibited efficient removal of organic matter (total COD>75%, soluble COD>88%, and LCFA>99%). Temperature reductions lead to significant reductions in microbiome diversity. Fermentative bacteria were highly dynamic and functional redundant during temperature reductions. Smithella was the dominant syntrophic bacteria involved in LCFA degradation coupled with Methanothrix and Methanocorpusculum at 15°C. Membrane modifications and compatible cellular solutes production were triggered by temperature reductions as microbial response to cold stress. This study provided molecular insights in microbial acclimation to low temperatures for psychrophilic AD.

Narrow-Band Imaging in Head and Neck Carcinomas: A Systematic Review and Meta-Analysis.

OBJECTIVE: The purpose of this study was to assess the diagnostic performance of narrow-band imaging (NBI) in monitoring patients with head and neck carcinomas posttreatment and to compare it with that of white light endoscopy (WLE). DATA SOURCES: PubMed, Embase, Web of Science (WOS), Cochrane Library, China Biology Medicine disc (CBM disc), China National Knowledge Internet (CNKI), Wanfang Data, China Science and Technology Journal Database (CSTJ), Chinese Clinical Trial Register. REVIEW METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), literature published before July 2024 was searched. Patients who underwent surgery, radiotherapy (RT), or chemo-RT for head and neck carcinomas with posttreatment follow-up using NBI were analyzed. The main outcomes were sensitivity, specificity, and diagnostic odds ratio (DOR) for NBI and WLE in posttreatment follow-up. RESULTS: The sensitivity, specificity, and DOR for NBI and WLE in posttreatment follow-up for head and neck carcinomas were 95% (95% confidence interval [CI]: 88%-98%), 96% (95% CI: 92%-98%), 433 (95% CI: 120-1560) and 72% (95% CI: 49%-87%), 72% (95% CI: 4%-99%), 7 (95% CI: 0-191). Additionally, the area under the curve (AUC) values for NBI and WLE were 0.99 (95% CI: 0.97-0.99) and 0.75 (95% CI: 0.71-0.79), respectively. The number of lesions and patients, treatment modality, follow-up time, disease, and endoscopic system might be sources of heterogeneity. CONCLUSION: Compared to WLE, NBI demonstrated superior diagnostic performance in follow-up patients with head and neck carcinoma posttreatment. NBI offers technical support and a clinical foundation for early detection of head and neck carcinoma recurrence. LEVEL OF EVIDENCE: NA Laryngoscope, 2024.

Identification of diagnostic biomarkers of rheumatoid arthritis based on machine learning-assisted comprehensive bioinformatics and its correlation with immune cells.

Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammatory cell infiltration, which can lead to chronic disability, joint destruction and loss of function. At present, the pathogenesis of RA is still unclear. The purpose of this study is to explore the potential biomarkers and immune molecular mechanisms of rheumatoid arthritis through machine learning-assisted bioinformatics analysis, in order to provide reference for the early diagnosis and treatment of RA disease. Methods: RA gene chips were screened from the public gene GEO database, and batch correction of different groups of RA gene chips was performed using Strawberry Perl. DEGs were obtained using the limma package of R software, and functional enrichment analysis such as gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), and gene set (GSEA) were performed. Three machine learning methods, least absolute shrinkage and selection operator regression (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest tree (Random Forest), were used to identify potential biomarkers of RA. The validation group data set was used to verify and further confirm its expression and diagnostic value. In addition, CIBERSORT algorithm was used to evaluate the infiltration of immune cells in RA and control samples, and the correlation between confirmed RA diagnostic biomarkers and immune cells was analyzed. Results: Through feature screening, 79 key DEGs were obtained, mainly involving virus response, Parkinson's pathway, dermatitis and cell junction components. A total of 29 hub genes were screened by LASSO regression, 34 hub genes were screened by SVM-RFE, and 39 hub genes were screened by Random Forest. Combined with the three algorithms, a total of 12 hub genes were obtained. Through the expression and diagnostic value verification in the validation group data set, 7 genes that can be used as diagnostic biomarkers for RA were preliminarily confirmed. At the same time, the correlation analysis of immune cells found that γδT cells, CD4+ memory activated T cells, activated dendritic cells and other immune cells were positively correlated with multiple RA diagnostic biomarkers, CD4+ naive T cells, regulatory T cells and other immune cells were negatively correlated with multiple RA diagnostic biomarkers. Conclusions: The results of novel characteristic gene analysis of RA showed that KYNU, EVI2A, CD52, C1QB, BATF, AIM2 and NDC80 had good diagnostic and clinical value for the diagnosis of RA, and were closely related to immune cells. Therefore, these seven DEGs may become new diagnostic markers and immunotherapy markers for RA.

MCNN_MC: Computational Prediction of Mitochondrial Carriers and Investigation of Bongkrekic Acid Toxicity Using Protein Language Models and Convolutional Neural Networks.

Mitochondrial carriers (MCs) are essential proteins that transport metabolites across mitochondrial membranes and play a critical role in cellular metabolism. ADP/ATP (adenosine diphosphate/adenosine triphosphate) is one of the most important carriers as it contributes to cellular energy production and is susceptible to the powerful toxin bongkrekic acid. This toxin has claimed several lives; for example, a recent foodborne outbreak in Taipei, Taiwan, has caused four deaths and sickened 30 people. The issue of bongkrekic acid poisoning has been a long-standing problem in Indonesia, with reports as early as 1895 detailing numerous deaths from contaminated coconut fermented cakes. In bioinformatics, significant advances have been made in understanding biological processes through computational methods; however, no established computational method has been developed for identifying mitochondrial carriers. We propose a computational bioinformatics approach for predicting MCs from a broader class of secondary active transporters with a focus on the ADP/ATP carrier and its interaction with bongkrekic acid. The proposed model combines protein language models (PLMs) with multiwindow scanning convolutional neural networks (mCNNs). While PLM embeddings capture contextual information within proteins, mCNN scans multiple windows to identify potential binding sites and extract local features. Our results show 96.66% sensitivity, 95.76% specificity, 96.12% accuracy, 91.83% Matthews correlation coefficient (MCC), 94.63% F1-Score, and 98.55% area under the curve (AUC). The results demonstrate the effectiveness of the proposed approach in predicting MCs and elucidating their functions, particularly in the context of bongkrekic acid toxicity. This study presents a valuable approach for identifying novel mitochondrial complexes, characterizing their functional roles, and understanding mitochondrial toxicology mechanisms. Our findings, that utilize computational methods to improve our understanding of cellular processes and drug-target interactions, contribute to the development of therapeutic strategies for mitochondrial disorders, reducing the devastating effects of bongkrekic acid poisoning.

Deciphering the Language of Protein-DNA Interactions: A Deep Learning Approach Combining Contextual Embeddings and Multi-Scale Sequence Modeling.

Deciphering the mechanisms governing protein-DNA interactions is crucial for understanding key cellular processes and disease pathways. In this work, we present a powerful deep learning approach that significantly advances the computational prediction of DNA-interacting residues from protein sequences. Our method leverages the rich contextual representations learned by pre-trained protein language models, such as ProtTrans, to capture intrinsic biochemical properties and sequence motifs indicative of DNA binding sites. We then integrate these contextual embeddings with a multi-window convolutional neural network architecture, which scans across the sequence at varying window sizes to effectively identify both local and global binding patterns. Comprehensive evaluation on curated benchmark datasets demonstrates the remarkable performance of our approach, achieving an area under the ROC curve (AUC) of 0.89 - a substantial improvement over previous state-of-the-art sequence-based predictors. This showcases the immense potential of pairing advanced representation learning and deep neural network designs for uncovering the complex syntax governing protein-DNA interactions directly from primary sequences. Our work not only provides a robust computational tool for characterizing DNA-binding mechanisms, but also highlights the transformative opportunities at the intersection of language modeling, deep learning, and protein sequence analysis. The publicly available code and data further facilitate broader adoption and continued development of these techniques for accelerating mechanistic insights into vital biological processes and disease pathways. In addition, the code and data for this work are available at https://github.com/B1607/DIRP.

Full-scale study on high-rate low-temperature anaerobic digestion of agro-food wastewater: process performances and microbial community.

The fast-growing global population has led to a substantial increase in food production, which generates large volumes of wastewater during the process. Despite most industrial wastewater being discharged at lower ambient temperatures (<20 °C), majority of the high-rate anaerobic reactors are operated at mesophilic temperatures (>30 °C). High-rate low-temperature anaerobic digestion (LtAD) has proven successful in treating industrial wastewater both at laboratory and pilot scales, boasting efficient organic removal and biogas production. In this study, we demonstrated the feasibility of two full-scale high-rate LtAD bioreactors treating meat processing and dairy wastewater, and the microbial communities in both reactors were examined. Both reactors exhibited rapid start-up, achieving considerable chemical oxygen demand (COD) removal efficiencies (total COD removal >80%) and generating high-quality biogas (CH4% in biogas >75%). Long-term operations (6-12 months) underscored the robustness of LtAD bioreactors even during winter periods (average temperature <12 °C), as evidenced by sustained high COD removal rates (total COD removal >80%). The stable performance was underpinned by a resilient microbial community comprising active acetoclastic methanogens, hydrolytic, and fermentative bacteria. These findings underscore the feasibility of high-rate low-temperature anaerobic wastewater treatment, offering promising solutions to the zero-emission wastewater treatment challenge.

Gasdermin D could be lost in the brain parenchyma infarct core and a pyroptosis-autophagy inhibition effect of Jie-Du-Huo-Xue decoction after stroke.

Background: The Chinese ethnic medicine Jie-Du-Huo-Xue Decoction (JDHXD) is used to alleviate neuroinflammation in cerebral ischemia (CI). Our previous studies have confirmed that JDHXD can inhibit microglial pyroptosis in CI. However, the pharmacological mechanism of JDHXD in alleviating neuroinflammation and pyroptosis needs to be further elucidated. New research points out that there is an interaction between autophagy and inflammasome NLRP3, and autophagy can help clear NLRP3. The NLRP3 is a key initiator of pyroptosis and autophagy. The effect of JDHXD promoting autophagy to clear NLRP3 to inhibit pyroptosis on cerebral ischemia-reperfusion inflammatory injury is currently unknown. We speculate that JDHXD can inhibit pyroptosis in CI by promoting autophagy to clear NLRP3. Methods: Chemical characterization of JDHXD was performed using LC-MS. Model of middle cerebral artery occlusion/reperfusion (MCAO/R) was established in SD rats. Neurological deficits, neuron damage, and cerebral infarct volume were evaluated. Western Blot and immunofluorescence were used to detect neuronal pyroptosis and autophagy. Results: 30 possible substance metabolites in JDHXD medicated serum were analyzed by LC-MS (Composite Score > 0.98). Furthermore, JDHXD protects rat neurological function and cerebral infarct size after CI. JDHXD inhibited the expression of pyroptosis and autophagy after CI. Our western blot and immunofluorescence results showed that JDHXD treatment can reduce the expression of autophagy-related factors ULK1, beclin1, and LC3-Ⅱ. The expression of NLRP3 protein was lower in the JDHXD group than in the I/R group. Compared with the I/R group, the expressions of pyroptosis-related factors caspase-1 P 10, GSDMD-NT, IL-18, and IL-1ß decreased in the JDHXD group. Furthermore, we observed an unexpected result: immunofluorescence demonstrated that Gasdermin D (GSDMD) was significantly absent in the infarct core, and highly expressed in the peri-infarct and contralateral cerebral hemispheres. This finding challenges the prevailing view that GSDMD is elevated in the ischemic cerebral hemisphere. Conclusion: JDHXD inhibited pyroptosis and autophagy after MCAO/R. JDHXD suppressed pyroptosis and autophagy by inhibiting NLRP3, thereby alleviating CI. In addition, we present a different observation from previous studies that the expression of GSDMD in the infarct core was lower than that in the peri-infarct and contralateral non-ischemic hemispheres on day 3 of CI.

[Simultaneous determination of maltol and seventeen saponins in red and black ginseng by UPLC-PDA wavelength switching method].

This study developed a UPLC-PDA wavelength switching method to simultaneously determine the content of maltol and seventeen saponins in red and black ginseng and compared the quality differences of two different processed products of red and black ginseng. A Waters HSS T3 column(2. 1 mm×100 mm, 1. 8 µm) at 30 ℃ was adopted, with the mobile phase of acetonitrile(A) and water containing 0. 1% phosphoric acid(B) under gradient elution, the flow rate of 0. 3 m L·min~(-1), and the injection volume of 2 µL.The wavelength switching was set at 273 nm within 0-11 min and 203 nm within 11-60 min. The content results of multiple batches of red and black ginseng samples were analyzed by the hierarchical cluster analysis(HCA) and principal component analysis(PCA) to evaluate the quality difference. The results showed that the 18 constituents exhibited good linear relationships within certain concentration ranges, with the correlation coefficients(r) greater than 0. 999 1. The relative standard deviations(RSDs) of precision,repeatability, and stability were all less than 5. 0%. The average recoveries ranged from 95. 93% to 104. 2%, with an RSD of 1. 8%-4. 2%. The content determination results showed that the quality of red and black ginseng samples was different, and the two types of processed products were intuitively distinguished by HCA and PCA. The method is accurate, reliable, and reproducible. It can be used to determine the content of maltol and seventeen saponins in red and black ginseng and provide basic information for the quality evaluation and comprehensive utilization of red and black ginseng.

Polysaccharides extracted from common buckwheat (Fagopyrum esculentum) attenuate cognitive impairment via suppressing RAGE/p38/NF-κB signaling and dysbiosis in AlCl3-treated rats.

Patients may find it challenging to accept several FDA-approved drugs for Alzheimer's disease (AD) treatment due to their unaffordable prices and side effects. Despite the known antioxidant, anti-inflammatory, and microbiota-regulating effects of common buckwheat (Fagopyrum esculentum) polysaccharides (FEP), their specific role in preventing AD has not been determined. Here, this study investigated the preventive effects of FEP on AD development in AlCl3-treated rats. The physical properties of FEP were evaluated using X-ray diffraction, FTIR, TGA, DSC, monosaccharide composition, molecular weight, and scanning electron microscopy. The results demonstrated that FEP administration improved memory and learning ability in AlCl3-treated rats. Additionally, AD pathological biomarkers (APP, BACE1, Aß1-42, and p-TauSer404), inflammatory-associated proteins (IL-1ß, IL-6, TNF-α, and Iba1), and MDA and the RAGE/p38/NF-κB pathway were elevated in AlCl3-treated rats. Moreover, these effects were reversed by the upregulation of LRP1, anti-inflammatory cytokines (IL-4 and IL-10), antioxidant enzymes (SOD and catalase), and autophagy proteins (Atg5, Beclin-1, and LC3B). Furthermore, FEP treatment increased the levels of short-chain fatty acids (SCFAs) and the abundance of SCFAs-producing microbes ([Eubacterium]_xylanophilum_group, Lachnospiraceae_NK4A136_group, Lactobacillus). Overall, FEP mitigated oxidative stress, RAGE/p38/NF-κB-mediated neuroinflammation, and AD-associated proteins by upregulating autophagy and SCFA levels, which led to the amelioration of cognitive impairment through microbiota-gut-brain communication in AlCl3-treated rats.

A new simplified risk assessment model enhances postoperative prophylaxis of venous thromboembolism in Chinese adult patients with inguinal hernia (CHAT-3): a prospective, multicenter, randomized controlled trial.

BACKGROUND: Venous thromboembolism (VTE) significantly affects the prognosis of surgical patients with inguinal hernia. The complex Caprini score, commonly used for postoperative VTE risk assessment, poses practical challenges for surgeons in clinical settings. METHODS: The CHAT-3 trial, a prospective, multicenter, randomized controlled trial, compared a simple three-factor model to assess VTE risk against routine practices in postinguinal hernia surgery (IHS) patients. The patients were randomly assigned (1:1) to the intervention or control arm. The intervention group used the three-factor model to identify patients at moderate or high risk of VTE for subsequent prophylaxis according to clinical guidelines. Both groups were followed for 4 weeks, with randomization implemented using computer-generated sequences. The primary outcome measured was the rate of VTE prophylaxis. Secondary outcomes included time spent on VTE risk assessment (surgeon self-reported), postoperative D-dimer trends, perioperative VTE occurrence, bleeding events, and the net clinical benefit. RESULTS: Of the 1109 participants, 508 in the experimental group and 601 in the control group completed follow-up. The three-factor model showed higher VTE prophylaxis rates in all patients (pharmacologic prophylaxis: 26.2 vs. 6.00%, P <0.001) and particularly in those at high risk (pharmacologic prophylaxis: 57.3 vs. 9.50%, P <0.001). The experimental group significantly reduced VTE risk assessment time compared to the Caprini score (1.39±0.55 min vs. 5.73±1.35 min, P <0.001). The experimental group had lower D-dimer levels (0.26±0.73 mg/l vs. 0.35±0.55 mg/l, P =0.028). In the experimental group, the patients did not experience an increased risk of VTE (0 vs. 1.66%, P =0.268) and bleeding (1.18 vs. 0.67%, P =0.558) compared to the controls. There was no significant difference in net clinical benefit, which combined VTE and bleeding events, between the experimental and control groups (1.18 vs. 0.83%, P =0.559). CONCLUSION: Applying the simple three-factor model in perioperative VTE management could quickly identify the patient with a high risk of VTE and improve the prophylaxis rate of perioperative VTE.

Comparison of learning curves and related postoperative indicators between endoscopic and robotic thyroidectomy: a systematic review and meta-analysis.

BACKGROUD: Endoscopic thyroidectomy (ET) and robotic thyroidectomy (RT) yield similar perioperative outcomes. This study investigated how the learning curve (LC) affects perioperative outcomes between ET and RT, identifying factors that influence the LC. MATERIALS AND METHODS: Two researchers individually searched PubMed, EMBASE, Web of Science, and Cochrane Library for relevant studies published until February 2024. The Newcastle-Ottawa Scale assessed study quality. Random effects model was used to compute the odds ratio and weighted mean difference (WMD). Poisson regression comparison of the number of surgeries (NLC) was required for ET and RT to reach the stable stage of the LC. Heterogeneity was measured using Cochran's Q. Publication bias was tested using funnel plots, and sensitivity analysis assessed findings robustness. Subgroup analysis was done by operation type and patient characteristics. RESULTS: This meta-analysis involved 33 studies. The drainage volume of ET was higher than that of RT (WMD=-17.56 [30.22, -4.49]). After reaching the NLC, the operation time of ET and RT was shortened (ET: WMD=28.15[18.04, 38.26]; RT: WMD=38.53[29.20, 47.86]). Other perioperative outcomes also improved to varying degrees. Notably, RT showed more refined central lymph node resection(5.67 vs. 4.71), less intraoperative bleeding (16.56 mL vs. 42.30 mL), and incidence of transient recurrent laryngeal nerve injury(24.59 vs. 26.77). The NLC of RT was smaller than that of ET(Incidence-rate ratios [IRR]=0.64[0.57, 0.72]). CUSUM analysis (ET: IRR=0.84[0.72, 0.99]; RT: IRR=0.55[0.44, 0.69]) or a smaller number of respondents (ET: IRR=0.26[0.15, 0.46]; RT: IRR=0.51[0.41, 0.63]) was associated with smaller NLC. In RT, transoral approach (IRR=2.73[1.96, 4.50]; IRR=2.48[1.61, 3.84]) and retroauricular approach (RAA) (IRR=2.13[1.26, 3.60]; IRR=1.78[1.04, 3.05]) had smaller NLC compared to bilateral axillo-breast and transaxillary approach (TAA). In ET, the NLC of RAA was smaller than that of TAA (IRR=1.61[1.04, 2.51]), breast approach(IRR=1.67[1.06, 2.64]), and subclavian approach(IRR=1.80[1.03, 3.14]). CONCLUSIONS: Rich surgical experience can improve surgical results of ET and RT. After reaching the NLC, the perioperative outcomes of RT are better than those of ET. Study subjects, surgical approaches, and analysis methods can affect NLC.

Association of sudden sensorineural hearing loss with meteorological factors: a time series study in Hefei, China, and a literature review.

Air pollution can cause disease and has become a major global environmental problem. It is currently believed that air pollution may be related to the progression of SSNHL. As a rapidly developing city in recent years, Hefei has serious air pollution. In order to explore the correlation between meteorological variables and SSNHL admissions, we conducted this study. This study investigated the short-term associations between SSNHL patients admitted to the hospital and Hefei climatic variables. The daily data on SSNHL-related hospital admissions and meteorological variables containing mean temperature (T-mean; °C), diurnal temperature range (DTR; °C), atmospheric pressure (AP; Hp), and relative humidity (RH; %), from 2014 to 2021 (2558 days), were collected. A time-series analysis integrating distributed lag non-linear models and generalized linear models was used. PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Literature published up to August 2023 was reviewed to explore the potential impact mechanisms of meteorological factors on SSNHL. The mechanisms were determined in detail, focusing on wind speed, air pressure, temperature, humidity, and air pollutants. Using a median of 50.00% as a baseline, the effect of exceedingly low T-mean in the single-day hysteresis effect model began at a lag of 8 days (RR = 1.032, 95% CI: 1.001 ~ 1.064). High DTR affected the admission rate for SSNHL on lag 0 day. The significance of the effect was the greatest on that day (RR = 1.054, 95% CI: 1.007 ~ 1.104) and then gradually decreased. High and exceedingly high RH affected the admission rate SSNHL on lag 0 day, and these effects lasted for 8 and 7 days, respectively. There were significant associations between all grades of AP and SSNHL. This is the first study to assess the effect of meteorological variables on SSNHL-related admissions in China using a time-series approach. Long-term exposures to high DTR, RH values, low T-mean values, and all AP grades enhance the incidence of SSNHL in residents. Limiting exposure to extremes of ambient temperature and humidity may reduce the number of SSNHL-related hospital visits in the region. It is advisable to maintain a suitable living environment temperature and avoid extreme temperature fluctuations and high humidity. During periods of high air pollution, it is recommended to stay indoors and refrain from outdoor exercise.

Activating transcription factor 6 contributes to cisplatin­induced ototoxicity via regulating the unfolded proteins response.

As a broad-spectrum anticancer drug, cisplatin is widely used in the treatment of tumors in various systems. Unfortunately, several serious side effects of cisplatin limit its clinical application, the most common of which are nephrotoxicity and ototoxicity. Studies have shown that cochlear hair cell degeneration is the main cause of cisplatin-induced hearing loss. However, the mechanism of cisplatin-induced hair cell death remains unclear. The present study aimed to explore the potential role of activating transcription factor 6 (ATF6), an endoplasmic reticulum (ER)-localized protein, on cisplatin-induced ototoxicity in vivo and in vitro. In this study, we observed that cisplatin exposure induced apoptosis of mouse auditory OC-1 cells, accompanied by a significant increase in the expression of ATF6 and C/EBP homologous protein (CHOP). In cell or cochlear culture models, treatment with an ATF6 agonist, an ER homeostasis regulator, significantly ameliorated cisplatin-induced cytotoxicity. Further, our in vivo experiments showed that subcutaneous injection of an ATF6 agonist almost completely prevented outer hair cell loss and significantly alleviated cisplatin-induced auditory brainstem response (ABR) threshold elevation in mice. Collectively, our results revealed the underlying mechanism by which activation of ATF6 significantly improved cisplatin-induced hair cell apoptosis, at least in part by inhibiting apoptosis signal-regulating kinase 1 expression, and demonstrated that pharmacological activation of ATF6-mediated unfolded protein response is a potential treatment for cisplatin-induced ototoxicity.

[Prognostic Value of IGF2BP3 Gene Expression Levels in Patients with Acute Myeloid Leukemia].

OBJECTIVE: To investigate the relationship between IGF2BP3 gene expression and prognosis in patients with acute myeloid leukemia (AML). METHODS: High throughput transcriptome sequencing was performed on bone marrow primary leukemia cells from 27 patients with AML in our center, the relationship between IGF2BP3 expression levels and clinical characteristics were analyzed and verify the samples from patients with newly treated AML and refractory AML. The expression level of IGF2BP3 gene were analyzed in 20 healthy subjects and 26 patients with AML. The expression of IGF2BP3 in two anthracycline-resistant cell lines (HL60/ADR, K562/ADR) was detected by RT-qPCR and Western blot, and the expression difference of IGF2BP3 was compared with that in sensitive cells (HL60, K562). The relationship between the expression level of IGF2BP3 in patients with AML and prognostic were analyzed through data analysis of 746 patients with AML, and the prognostic value of IGF2BP3 in AML was analyzed by multivariate Cox regression analysis. RESULTS: In the bone marrow primary leukemia cells of 27 AML patients in our center, the expression level of IGF2BP3 in patients with refractory AML was significantly higher than that in chemotherapy sensitive patients (P =0.0343). The expression of IGF2BP3 in leukemia patients with extramedullary infiltration (EMI) was significantly higher than that in AML patients without extramedullary infiltration (P =0.0049). Compared with healthy subjects (n=20), IGF2BP3 expression in AML patients (n=26) was higher (P =0.0009). The expression of IGF2BP3 mRNA in the anthracycline resistant cell lines (HL60/ADR, K562/ADR) was significantly higher than that in the sensitive cell lines (K562/ADR vs K562,P =0.0430; HL60/ADR vs HL60, P =0.7369). Western blot results showed that the expression of IGF2BP3 protein in mycin resistant cells was significantly higher than that in sensitive cells (P < 0.001). qPCR results showed that the expression level of IGF2BP3 mRNA in refractory AML patients was significantly higher than that in patients with chemotherapy sensitive (P =0.002). High expression of IGF2BP3 was associated with poor prognosis in AML (P < 0.05) in 3 large sample cohorts of AML patients. Univariate and multivariate prognostic analyses demonstrated that high expression of IGF2BP3 was significantly associated with shorter event-free survival (EFS, HR=1.887, P =0.024) and overall survival (OS, HR=1.619, P =0.016). CONCLUSION: The high expression of IGF2BP3 gene may be an important factor in the poor prognosis of AML, suggesting that IGF2BP3 gene may be a new molecular marker for the clinical prognosis evaluation and treatment strategy of AML.

Current advances and future directions in combined hepatocellular and cholangiocarcinoma.

The low incidence of combined hepatocellular cholangiocarcinoma (cHCC-CCA) is an important factor limiting research progression. Our study extensively included nearly three decades of relevant literature and assembled the most comprehensive database comprising 5,742 patients with cHCC-CCA. We summarized the characteristics, tumor markers, and clinical features of these patients. Additionally, we present the evolution of cHCC-CCA classification and explain the underlying rationale for these classification standards. We reviewed cHCC-CCA diagnostic advances using imaging features, tumor markers, and postoperative pathology, as well as treatment options such as surgical, adjuvant, and immune-targeted therapies. In addition, recent advances in more effective chemotherapeutic regimens and immune-targeted therapies were explored. Furthermore, we described the molecular mutation features and potential specific markers of cHCC-CCA. The prognostic value of Nestin has been proven, and we speculate that Nestin will also play a role in classification and diagnosis. However, further research is needed. Moreover, we believe that the possibility of using machine learning liquid biopsy for preoperative diagnosis and establishing a scoring system are directions for future research.