Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.

OBJECTIVES: This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis. METHODS: Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability. RESULTS: Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes. CONCLUSIONS: Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary. CLINICAL RELEVANCE STATEMENT: Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies. RESEARCH REGISTRATION UNIQUE IDENTIFYING NUMBER (UIN): International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.

Steroid-Refractory Myocarditis Induced by Immune Checkpoint Inhibitor Responded to Infliximab: Report of Two Cases and Literature Review.

Immune checkpoint inhibitors (ICIs), including anti-programmed cell death protein 1 and its ligand (PD-1/PD-L1) as well as anti-cytotoxic T lymphocyte-associated protein 4 (CTLA-4), have been widely used for treating solid tumors. Myocarditis is a potentially lethal immune-related adverse events (irAEs) caused by ICIs therapy. The treatment of steroid-refractory myocarditis is challenging. We reported two non-small-cell lung cancer patients with steroid-refractory myocarditis induced by ICI. The symptoms were not resolved after pulse corticosteroid therapy and subsequent treatment including intravenous immunoglobulin and mycophenolate mofetil. Considering the level of serum interleukin (IL)-6 decreased by > 50% and level of serum tumor necrosis factor-α (TNF-α) increased during the course of the disease, infliximab was used. Myocarditis gradually alleviated after infliximab treatment. The cases revealed that specific cytokine inhibitors have promising roles in the treatment of steroid-refractory myocarditis. Infliximab could be considered for patients with low level of IL-6 and elevated level of TNF-α.

Electrostatically Self-Assembled Magnetic Nanoparticles for High-Temperature Resistant and Friction-Controlled Lubrication System.

Magnetic-responsive surfactants are considered promising smart lubricating materials due to their significant stimulation response to applied magnetic fields. In this study, four magneto-responsive surfactants are successfully fabricated and encapsulated on the surface of molybdenum disulfide nanosheets (MoS2@C18H37N+(CH3)3[XCl3Br]-, X = Fe, Ce, Gd, and Ho) as base-oil components using electrostatic self-assembly, thereby constructing a multi-functional magnetic lubrication system (MoS2@STAX). Magnetorheological measurements confirm the remarkable responsiveness of MoS2@STACe lubricants at high shear rates and applied magnetic fields, which is further corroborated by the constant proximity of the magnet. The formation of dense carbon and tribo-chemical films between the friction interfaces at elevated temperatures is the primary factor contributing to the significant reduction in frictional wear. Notably, the magnetic lubricant demonstrates a pronounced response behavior when subjected to an applied magnetic field in the ceramic tribopair, even at lower magnetic fields. This work presents concepts for the development of high-temperature resistant and tunable lubrication additives by designing the material structure and controlling the magnetic stimulation.

Global research trends on the associations between the microbiota and lung cancer: a visualization bibliometric analysis (2008-2023).

Numerous papers have been published on the microbiota in lung cancer in recent years. However, there is still a lack of bibliometric analysis of the microbiota in lung cancer in this field. Our paper did bibliometric analyses and elucidated the knowledge structure and study hotspots related to the microbiota in lung cancer patients. We screened publications reporting on the microbiota in lung cancer from 2008 to 2023 from the Web of Science Core Collection (WoSCC) database, and carried out bibliometric analyses by the application of the VOSviewers, CiteSpace and R package "bibliometrix." The 684 documents enrolled in the analysis were obtained from 331 institutions in 67 regions by 4,661 authors and were recorded in 340 journals. Annual papers are growing rapidly, and the countries of China, the United States and Italy are contributing the most to this area of research. Zhejiang University is the main research organization. Science and Cancer had significant impacts on this area. Zhang Yan had the most articles, and the Bertrand Routy had the most co-cited times. Exploring the mechanism of action of the lung and/or gut microbiota in lung cancer and therapeutic strategies involving immune checkpoint inhibitors in lung cancer are the main topics. Moreover, "gut microbiota," "immunotherapy," and "short-chain fatty acids" are important keywords for upcoming study hotspots. In conclusion, microbiota research offers promising opportunities in lung cancer, with pivotal studies exploring the mechanisms that link lung and gut microbiota to therapeutic strategies, particularly through immune checkpoint inhibitors. Moreover, the gut-lung axis emerges as a novel target for innovative treatments. Further research is essential to unravel the detailed mechanisms of this connection.

Species-specific model based on sequence and structural information for ubiquitination sites prediction.

Ubiquitination is a common post-translational modification of proteins in eukaryotic cells, and it is also a significant method of regulating protein biological function. Computational methods for predicting ubiquitination sites can serve as a cost-effective and time-saving alternative to experimental methods. Existing computational methods often build classifiers based on protein sequence information, physical and chemical properties of amino acids, evolutionary information, and structural parameters. However, structural information about most proteins cannot be found in existing databases directly. The features of proteins differ among species, and some species have small amounts of ubiquitinated proteins. Therefore, it is necessary to develop species-specific models that can be applied to datasets with small sample sizes. To solve these problems, we propose a species-specific model (SSUbi) based on a capsule network, which integrates proteins' sequence and structural information. In this model, the feature extraction module is composed of two sub-modules that extract multi-dimensional features from sequence and structural information respectively. In the submodule, the convolution operation is used to extract encoding dimension features, and the channel attention mechanism is used to extract feature map dimension features. After integrating the multi-dimensional features from both types of information, the species-specific capsule network further converts the features into capsule vectors and classifies species-specific ubiquitination sites. The experimental results show that SSUbi can effectively improve the prediction performance of species with small sample sizes and outperform other models.

Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography.

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.

Diagnostic Value of Artificial Intelligence in Minimal Breast Lesions Based on Real-Time Dynamic Ultrasound Imaging.

Purpose: : To explore the diagnostic value of artificial intelligence (AI)-based on real-time dynamic ultrasound imaging system for minimal breast lesions. Patients and Methods: Minimal breast lesions with a maximum diameter of ≤10mm were selected in this prospective study. The ultrasound equipment and AI system were activated Simultaneously. The ultrasound imaging video is connected to the server of AI system to achieve simultaneous output of AI and ultrasound scanning. Dynamic observation of breast lesions was conducted via ultrasound. And these lesions were evaluated and graded according to the Breast Imaging Reporting and Data System (BI-RADS) classification system through deep learning (DL) algorithms in AI. Surgical pathology was taken as the gold standard, and ROC curves were drawn to determine the area under the curve (AUC) and the optimal threshold values of BI-RADS. The diagnostic efficacy was compared with the use of a BI-RADS category >3 as the threshold for clinically intervening in diagnosing minimal breast cancers. Results: 291 minimal breast lesions were enrolled in the study, of which 228 were benign (78.35%) and 63 were malignant (21.65%). The AUC of the ROC curve was 0.833, with the best threshold value >4A. When using >BI-RADS 3 and >BI-RADS 4A as threshold values, the sensitivity and negative predictive value for minimal breast cancers were higher for >BI-RADS 3 than >BI-RADS 4A (100% vs 65.08%, 100% vs 89.91%, P values <0.001). However, the corresponding specificity, positive predictive value, and accuracy were lower than those for >BI-RADS 4A (42.11% vs 85.96%, 32.31% vs 56.16%, and 54.64% vs 81.44%, P values <0.001). Conclusion: The AI-based real-time dynamic ultrasound imaging system shows good capacity in diagnosing minimal breast lesions, which is helpful for early diagnosis and treatment of breast cancer, and improves the prognosis of patients. However, it still results in some missed diagnoses and misdiagnoses of minimal breast cancers.

Effects of plant nutrient acquisition strategies on biomass allocation patterns in wetlands along successional sequences in the semi-arid upper Yellow River basin.

The ecological environment of wetlands in semi-arid regions has deteriorated, and vegetation succession has accelerated due to climate warming-induced aridification and human interference. The nutrient acquisition strategies and biomass allocation patterns reflect plant growth strategies in response to environmental changes. However, the impact of nutrient acquisition strategies on biomass allocation in successional vegetation remains unclear. We investigated 87 plant communities from 13 wetland sites in the semi-arid upper Yellow River basin. These communities were divided into three successional sequences: the herbaceous community (HC), the herbaceous-shrub mixed community (HSC), and the shrub community (SC). The nutrient composition of stems and leaves, as well as the biomass distribution above and belowground, were investigated. Results revealed that aboveground biomass increased with succession while belowground biomass decreased. Specifically, SC exhibited the highest stem biomass of 1,194.53 g m-2, while HC had the highest belowground biomass of 2,054.37 g m-2. Additionally, significant positive correlations were observed between leaf and stem biomasses in both HC and SC. The nitrogen (N) and phosphorus (P) contents within aboveground parts displayed an evident upward trend along the succession sequence. The highest N and P contents were found in SC, followed by HSC, and the lowest in HC. Stem N was negatively correlated with stem, leaf, and belowground biomass but positively correlated with root-shoot ratio. Leaf P displayed positive correlations with aboveground biomass while showing negative correlations with belowground biomass and root-shoot ratio. The ratios of C:N, C:P, and N:P in stem and leaf exhibited positive correlations with belowground biomass. The random forest model further demonstrated that stem N and leaf P exerted significant effects on aboveground biomass, while leaf P, stem N and P, and leaf C:P ratio had significant effects on belowground components. Additionally, the root-shoot ratio was significantly influenced by leaf P, leaf C:P ratio, and stem N, P, and C:P ratio. Therefore, the aboveground and belowground biomasses exhibited asynchronism across successional sequences, while plant nutrient acquisition strategies, involving nutrient levels and stoichiometric ratios, determined the biomass allocation pattern. This study offers valuable insights for assessing vegetation adaptability and formulating restoration plans in the semi-arid upper Yellow River basin.

Incidence and risk factors of VTE in lung cancer: a meta-analysis.

BACKGROUND: Lung cancer has maintained a high prevalence and mortality. Besides, venous thromboembolism (VTE) is the third most common disease of cardiovascular disease. Lung cancer with VTE usually influenced the overall survival in the follow-up. In the development of lung cancer, vigilance against and early diagnosis of VTE is of significance. METHODS: We searched the databases of PubMed, Web of Science, Embase and Cochrane for related research up to 30 November 2023 and extracted information of incidence, odds ratio (OR), hazard ratio (HR) and their 95% confidence intervals (CIs), for evaluating the incidence of VTE and its risk factors. RESULTS: A total of 54 articles and 873,292 records were included in our study. The pooled incidences of VTE and PE were 6% and 3%, respectively. Subgroup analysis revealed that the tumour, node and metastasis (TNM) stage (HR= 5.43, 95% CI: 2.42, 12.22), metastasis (HR= 2.67, 95% CI: 1.35, 5.29) and chemotherapy (HR= 2.27, 95% CI: 1.11, 4.65) had major influence on VTE occurrence. CONCLUSIONS: Lung cancer complicated with VTE is unignorable, and its occurrence varies widely by tumour staging, tissue type and treatment. The results may aid in clinical decision-making about lung cancer in higher risk with VTE and weather receiving anticoagulant prophylaxis.

The pooled incidences of VTE and PE were 6% and 3% in lung cancer.LUAD, NSCLC and tumour stage III-IV have significant relevant with VTE in lung cancer.

The Impact of Early Antibiotic Use on Clinical Outcomes of Patients Hospitalized with COVID-19: A Propensity Score-Matched Analysis.

Purpose: Early empiric antibiotics were prescribed to numerous patients during the Coronavirus disease 2019 (COVID-19) pandemic. However, the potential impact of empiric antibiotic therapy on the clinical outcomes of patients hospitalized with COVID-19 is yet unknown. Methods: In this retrospective cohort study, early antibiotics use cohort was defined as control group, which was compared with no antibiotic use and delayed antibiotic use cohorts for all-cause mortality during hospitalization. The 1:2 propensity score matched patient populations were further developed to adjust confounding factors. Survival curves were compared between different cohorts using a Log rank test to assess the early antibiotic effectiveness. Results: We included a total of 1472 COVID-19 hospitalized patients, of whom 87.4% (1287 patients) received early antibiotic prescriptions. In propensity-score-matched datasets, our analysis comprised 139 patients with non-antibiotic use (with 278 matched controls) and 27 patients with deferred-antibiotic use (with 54 matched controls). Patients with older ages, multiple comorbidities, severe and critical COVID-19 subtypes, higher serum infection indicators, and inflammatory indicators at admission were more likely to receive early antibiotic prescriptions. After adjusting confounding factors likely to influence the prognosis, there is no significant difference in all-cause mortality (HR=1.000(0.246-4.060), p = 1.000) and ICU admission (HR=0.436(0.093-2.04), p = 0.293), need for mechanical ventilation (HR=0.723(0.296-1.763), p = 0.476) and tracheal intubation (HR=1.338(0.221-8.103), p = 0.751) were observed between early antibiotics use cohort and non-antibiotic use cohort. Conclusion: Early antibiotics were frequently prescribed to patients in more severe disease condition at admission. However, early antibiotic treatment failed to demonstrate better clinical outcomes in hospitalized patients with COVID-19 in the propensity-score-matched cohorts.

What are the frequent complaints voiced by inpatients and physicians amidst the emerging infectious diseases? - An illustrative instance of the COVID-19.

INTRODUCTION: Emerging infectious diseases (EIDs) can disrupt the healthcare system, causing regulatory changes that affect the healthcare-seeking process and potentially increase patient-physician dissatisfaction. This study aimed to collect and analyze patients' and physicians' complaints during an EID outbreak to inform potential clues regarding medical quality and patient safety enhancement in future dealing with EIDs, employing text mining methodologies. METHODS: In this descriptive study, complaint records from January 2020 to February 2023 at West China Hospital, a national medical facility in China, were analyzed. Patient and physician complaints have been retrospectively retrieved from the record from the medical department, and then categorized into distinct groups based on reporting reasons, encompassing COVID-19-related policies, healthcare access, availability of medical resources, and financial concerns. RESULTS: During the COVID-19 pandemic, 541 COVID-19-related complaints were identified: 330 (61.00%) from patients and 211 (39.00%) from physicians. The monthly volume of complaints fluctuated, starting at 10 in 2020, peaking at 21 in 2022, and dropping to 14 in 2023. Most complaints from inpatients were expressed by older males aged 40 to 65 (38.82%, 210/541). The primary source of complaints was related to mandatory COVID-19 policies (79.30%, 429/541), followed by concerns regarding timely healthcare services (31.61%, 171/541). Few complaints were expressed regarding the insufficiency of medical resources (2.77%, 15/541) and the high costs (4.25%, 23/541). The frequency of complaints expressed by doctors and patients in the emergency department was higher compared with other departments (24.58%, 133/541). CONCLUSIONS: Increased complaints may serve as a primitive and timely resource for investigating the potential hazards and drawbacks associated with policies pertaining to EIDs. Prompt collection and systematical analysis of patient and physician feedback could help us accurately evaluate the efficacy and repercussions of these policies. Implementing complaints-based assessment might improve care standards in forthcoming healthcare environments grappling with EIDs.

Single-cell transcriptome analysis deciphers the CD74-mediated immune evasion and tumour growth in lung squamous cell carcinoma with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) contributes to the incidence and prognosis of lung cancer. The presence of COPD significantly increases the risk of lung squamous cell carcinoma (LSCC). COPD may promote an immunosuppressive microenvironment in LSCC by regulating the expression of immune-inhibitory factors in T cells, although the mechanisms remain unclear. In this study, we aimed to decipher the tumour microenvironment signature for LSCC with COPD at a single-cell level. METHODS: We performed single-cell RNA sequencing on tumour tissues from LSCC with or without COPD, then investigated the features of the immune and tumour cells. We employed multiple techniques, including multispectral imaging, flow cytometry, tissue microarray analysis, survival analysis, co-culture systems and in vitro and in vivo treatment experiments, to validate the findings obtained from single-cell analyses. RESULTS: LSCC with COPD showed increased proportions of tumour-associated macrophages (TAMs) and higher levels of CD8+ T cell exhaustion molecules, which contributed to an immunosuppressive microenvironment. Further analysis revealed a critical cluster of CD74+ tumour cells that expressed both epithelial and immune cell signatures, exhibited a stronger capacity for tumorigenesis and predicted worse overall survival. Notably, migration inhibitory factor (MIF) secreted by TAMs from LSCC with COPD may promote the activation of CD74. MIF-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-programmed cell death-1 ligand 1 signalling pathway, facilitating tumour proliferation and immune evasion. CONCLUSIONS: Our comprehensive picture of the tumour ecosystem in LSCC with COPD provides deeper insights into relevant immune evasion mechanisms and potential targets for immunotherapy. HIGHLIGHT: Our results demonstrated higher proportions of tumour-associated macrophages (TAMs) and higher levels of exhaustion molecules in CD8+ T cells in the microenvironment of LSCC with COPD. CD74+tumour cells were associated with poor disease prognosis. Migration inhibitory factor (MIF)-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-PD-L1 signalling pathway, facilitating immune evasion.

An anti-corrosive cellulose nanocrystal/carbon nanotube derived Zn anode interface for dendrite-free aqueous Zn-ion batteries.

Notorious zinc dendrite growth and hydrogen precipitation reactions disrupt the galvanic/stripping process at the electrolyte/electrode interface, which seriously affects the cycling stability of zinc anodes in aqueous zinc ion batteries. To improve the stability and reversibility of zinc anodes, we report an artificial SEI consisting of hydrophobic carbon nanocrystals and highly conductive carbon nanotube networks. This interfacial hydrophobicity effectively excludes free water from the surface of the zinc anode, which prevents water erosion and reduces the interfacial side reactions, resulting in a significant improvement in the cycling stability and coulombic efficiency of Zn plating/stripping. Benefiting from the reversible proton storage and fast desolvation kinetic behavior of the CNC/CNT interlayer, the stable cycling time of Zn/Zn symmetric batteries exceeds 700 h even at a high current density of 5 mA cm-2. The assembled CNC/CNT@Zn‖V2O5 full cell maintains a high capacity of 101.1 mA h g-1 after 5000 cycles (1.0 mA g-1). This study opens up a new area for expanding the use of organic compounds in zinc anode protection and offers a promising strategy for accelerating the development of aqueous zinc-ion batteries.

Public Heterogeneous Preferences for Low-Dose Computed Tomography Lung Cancer Screening Service Delivery in Western China: A Discrete Choice Experiment.

BACKGROUND: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) is an efficient method that can reduce lung cancer mortality in high-risk individuals. However, few studies have attempted to measure the preferences for LDCT LCS service delivery. This study aimed to generate quantitative information on the Chinese population's preferences for LDCT LCS service delivery. METHODS: The general population aged 40 to 74 in the Sichuan province of China was invited to complete an online discrete choice experiment (DCE). The DCE required participants to answer 14 discrete choice questions comprising five attributes: facility levels, facility ownership, travel mode, travel time, and out-of-pocket cost. Choice data were analyzed using mixed logit and latent class logit (LCL) models. RESULTS: The study included 2529 respondents, with 746 (29.5%) identified as being at risk for lung cancer. Mixed logit model (MLM) analysis revealed that all five attributes significantly influenced respondents' choices. Facility levels had the highest relative importance (44.4%), followed by facility ownership (28.1%), while out-of-pocket cost had the lowest importance (6.4%). The at-risk group placed relatively more importance on price and facility ownership compared to the non-risk group. LCL model identified five distinct classes with varying preferences. CONCLUSION: This study revealed significant heterogeneity in preferences for LCS service attributes among the Chinese population, with facility level and facility ownership being the most important factors. The findings underscore the need for tailored strategies targeting different subgroup preferences to increase screening participation rates and improve early detection outcomes.

Lactate Oxidase Disrupts Lactate-Activated RAS and PI3K Oncogenic Signaling.

LOX was recently shown to inhibit cancer cell proliferation and tumor growth. The mechanism of this inhibition, however, has been exclusively attributed to LOX depletion of TME lactate, a cancer cell energy source, and production of H2O2, an oxidative stressor. We report that TME lactate triggers the assembly of the lactate receptor hydroxycarboxylic acid receptor 1 (HCAR1)-associated protein complex, which includes GRB2, SOS1, KRAS, GAB1, and PI3K, for the activation of both the RAS and the PI3K oncogenic signaling pathways in breast cancer (BCa) cells. LOX treatment decreased the levels of the proteins in the protein complex via induction of their proteasomal degradation. In addition, LOX inhibited lactate-stimulated expression of the lactate transporters MCT1 and MCT4. Our data suggest that HCAR1 activation by lactate is crucial for the assembly and function of the RAS and PI3K signaling nexus. Shutting down lactate signaling by disrupting this nexus could be detrimental to cancer cells. HCAR1 is therefore a promising target for the control of the RAS and the PI3K signaling required for BCa progression. Thus, our study provides insights into lactate signaling regulation of cancer progression and extends our understanding of LOX's functional mechanisms that are fundamental for exploring its therapeutic potential.

CRISPR Screening and Comparative LC-MS Analysis Identify Genes Mediating Efficacy of Delamanid and Pretomanid against Mycobacterium tuberculosis.

Tuberculosis (TB), the leading cause of death from bacterial infections worldwide, results from infection with Mycobacterium tuberculosis (Mtb). The antitubercular agents delamanid (DLM) and pretomanid (PMD) are nitroimidazole prodrugs that require activation by an enzyme intrinsic to Mtb; however, the mechanism(s) of action and the associated metabolic pathways are largely unclear. Profiling of the chemical-genetic interactions of PMD and DLM in Mtb using combined CRISPR screening reveals that the mutation of rv2073c increases susceptibility of Mtb to these nitroimidazole drugs both in vitro and in infected mice, whereas mutation of rv0078 increases drug resistance. Further assays show that Rv2073c might confer intrinsic resistance to DLM/PMD by interfering with inhibition of the drug target, decaprenylphophoryl-2-keto-b-D-erythro-pentose reductase (DprE2), by active nicotinamide adenine dinucleotide (NAD) adducts. Characterization of the metabolic pathways of DLM/PMD in Mtb using a combination of chemical genetics and comparative liquid chromatography-mass spectrometry (LC-MS) analysis of DLM/PMD metabolites reveals that Rv0077c, which is negatively regulated by Rv0078, mediates drug resistance by metabolizing activated DLM/PMD. These results might guide development of new nitroimidazole prodrugs and new regimens for TB treatment.

SFPL: Sample-specific fine-grained prototype learning for imbalanced medical image classification.

Imbalanced classification is a common and difficult task in many medical image analysis applications. However, most existing approaches focus on balancing feature distribution and classifier weights between classes, while ignoring the inner-class heterogeneity and the individuality of each sample. In this paper, we proposed a sample-specific fine-grained prototype learning (SFPL) method to learn the fine-grained representation of the majority class and learn a cosine classifier specifically for each sample such that the classification model is highly tuned to the individual's characteristic. SFPL first builds multiple prototypes to represent the majority class, and then updates the prototypes through a mixture weighting strategy. Moreover, we proposed a uniform loss based on set representations to make the fine-grained prototypes distribute uniformly. To establish associations between fine-grained prototypes and cosine classifier, we propose a selective attention aggregation module to select the effective fine-grained prototypes for final classification. Extensive experiments on three different tasks demonstrate that SFPL outperforms the state-of-the-art (SOTA) methods. Importantly, as the imbalance ratio increases from 10 to 100, the improvement of SFPL over SOTA methods increases from 2.2% to 2.4%; as the training data decreases from 800 to 100, the improvement of SFPL over SOTA methods increases from 2.2% to 3.8%.

Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.

BACKGROUND: With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application. METHODS: Our study encompassed a cohort of 23 336 patients who underwent chest low-dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI-assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules. RESULTS: The AI-assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001). CONCLUSIONS: Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI-assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.

Current status and progress of PD-L1 detection: guiding immunotherapy for non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths and represents a substantial disease burden worldwide. Immune checkpoint inhibitors combined with chemotherapy are the standard first-line therapy for advanced NSCLC without driver mutations. Programmed death-ligand 1 (PD-L1) is currently the only approved immunotherapy marker. PD-L1 detection methods are diverse and have developed rapidly in recent years, such as improved immunohistochemical detection methods, the application of liquid biopsy in PD-L1 detection, genetic testing, radionuclide imaging, and the use of machine learning methods to construct PD-L1 prediction models. This review focuses on the detection methods and challenges of PD-L1 from different sources, and discusses the influencing factors of PD-L1 detection and the value of combined biomarkers. Provide support for clinical screening of immunotherapy-advantage groups and formulation of personalized treatment decisions.

PPCRKB: a risk factor knowledge base of postoperative pulmonary complications.

Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk factors frequently occurring after surgical interventions, resulting in significant financial burdens, prolonged hospitalization and elevated mortality rates. Despite the existence of multiple studies on PPCs, a comprehensive knowledge base that can effectively integrate and visualize the diverse risk factors associated with PPCs is currently lacking. This study aims to develop an online knowledge platform on risk factors for PPCs (Postoperative Pulmonary Complications Risk Factor Knowledge Base, PPCRKB) that categorizes and presents the risk and protective factors associated with PPCs, as well as to facilitate the development of individualized prevention and management strategies for PPCs based on the needs of each investigator. The PPCRKB is a novel knowledge base that encompasses all investigated potential risk factors linked to PPCs, offering users a web-based platform to access these risk factors. The PPCRKB contains 2673 entries, 915 risk factors that have been categorized into 11 distinct groups. These categories include habit and behavior, surgical factors, anesthetic factors, auxiliary examination, environmental factors, clinical status, medicines and treatment, demographic characteristics, psychosocial factors, genetic factors and miscellaneous factors. The PPCRKB holds significant value for PPC research. The inclusion of both quantitative and qualitative data in the PPCRKB enhances the ability to uncover new insights and solutions related to PPCs. It could provide clinicians with a more comprehensive perspective on research related to PPCs in future. Database URL: http://sysbio.org.cn/PPCs.