Investigating causal associations between pneumonia and lung cancer using a bidirectional mendelian randomization framework.

BACKGROUND: Pneumonia and lung cancer are both major respiratory diseases, and observational studies have explored the association between their susceptibility. However, due to the presence of potential confounders and reverse causality, the comprehensive causal relationships between pneumonia and lung cancer require further exploration. METHODS: Genome-wide association study (GWAS) summary-level data were obtained from the hitherto latest FinnGen database, COVID-19 Host Genetics Initiative resource, and International Lung Cancer Consortium. We implemented a bidirectional Mendelian randomization (MR) framework to evaluate the causal relationships between several specific types of pneumonia and lung cancer. The causal estimates were mainly calculated by inverse-variance weighted (IVW) approach. Additionally, sensitivity analyses were also conducted to validate the robustness of the causalty. RESULTS: In the MR analyses, overall pneumonia demonstrated a suggestive but modest association with overall lung cancer risk (Odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.01 - 1.44, P = 0.037). The correlations between specific pneumonia types and overall lung cancer were not as significant, including bacterial pneumonia (OR: 1.07, 95% CI: 0.91 - 1.26, P = 0.386), viral pneumonia (OR: 1.00, 95% CI: 0.95 - 1.06, P = 0.891), asthma-related pneumonia (OR: 1.18, 95% CI: 0.92 - 1.52, P = 0.181), and COVID-19 (OR: 1.01, 95% CI: 0.78 - 1.30, P = 0.952). Reversely, with lung cancer as the exposure, we observed that overall lung cancer had statistically crucial associations with bacterial pneumonia (OR: 1.08, 95% CI: 1.03 - 1.13, P = 0.001) and viral pneumonia (OR: 1.09, 95% CI: 1.01 - 1.19, P = 0.037). Sensitivity analysis also confirmed the robustness of these findings. CONCLUSION: This study has presented a systematic investigation into the causal relationships between pneumonia and lung cancer subtypes. Further prospective study is warranted to verify these findings.

The real-world treatment characteristic and efficacy of immune checkpoint inhibitors in non-small cell lung cancer: Data from a retrospective cohort study.

BACKGROUND: The efficacy and prognosis of immune checkpoint inhibitors (ICIs) remain unresolved issues. Here, we assessed the treatment characteristics and efficacy of ICIs in non-small cell lung cancer (NSCLC) using real-world data and evaluated the predictive value of factors, including programmed death-ligand 1 (PD-L1) expression, for the clinical outcome of ICIs in NSCLC. METHODS: Analyzed data was collected from hospitalized patients in the West China Hospital of Sichuan University between January 2017 and March 2023. The Kaplan-Meier method was utilized for analyzing real-world progression-free survival (rwPFS), while Cox regression models was employed to access the correlation between the efficacy of immunotherapy and sociodemographic characteristics, disease information, and characteristics of ICI treatment. RESULTS: A total of 545 patients were included in the retrospective study and characteristics of immunotherapy varied significantly among PD-L1 expression groups. The median rwPFS for the entire population was 9.76 months. Subgroup analyses revealed that patients with high PD-L1 expression, early TNM stage, first-line immunotherapy, EGFR wild-type and those who have not received radiotherapy and targeted therapy previously were more likely to have better rwPFS. Furthermore, multivariate Cox regression analyses identified PD-L1 expression, EGFR mutation status and previous radiotherapy as the most influential predictors of the response to ICI treatment. CONCLUSIONS: This study presents the real-world experience of Chinese NSCLC patients undergoing ICI treatment, offering guidance for clinical decision-making based on various patient conditions, preferences, and indications for ICIs, through the evaluation of immunotherapy efficacy and predictors in NSCLC patients.

Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.

Carnitine functions as an enhancer of NRF2 to inhibit osteoclastogenesis via regulating macrophage polarization in osteoporosis.

Osteoporosis, which manifests as reduced bone mass and deteriorated bone quality, is common in the elderly population. It is characterized by persistent elevation of macrophage-associated inflammation and active osteoclast bone resorption. Currently, the roles of intracellular metabolism in regulating these processes remain unclear. In this study, we initially performed bioinformatics analysis and observed a significant increase in the proportion of M1 macrophages in bone marrow with aging. Further metabolomics analysis demonstrated a notable reduction in the expression of carnitine metabolites in aged macrophages, while carnitine was not detected in osteoclasts. During the differentiation process, osteoclasts took up carnitine synthesized by macrophages to regulate their own activity. Mechanistically, carnitine enhanced the function of Nrf2 by inhibiting the Keap1-Nrf2 interaction, reducing the proteasome-dependent ubiquitination and degradation of Nrf2. In silico molecular ligand docking analysis of the interaction between carnitine and Keap1 showed that carnitine binds to Keap1 to stabilize Nrf2 and enhance its function. In this study, we found that the decrease in carnitine levels in aging macrophages causes overactivation of osteoclasts, ultimately leading to osteoporosis. A decrease in serum carnitine levels in patients with osteoporosis was found to have good diagnostic and predictive value. Moreover, supplementation with carnitine was shown to be effective in the treatment of osteoporosis.

Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study.

Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. Methods: 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. Findings: The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all). Interpretation: The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients. Funding: This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).

The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID.

There have been hundreds of millions of cases of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the growing population of recovered patients, it is crucial to understand the long-term consequences of the disease and management strategies. Although COVID-19 was initially considered an acute respiratory illness, recent evidence suggests that manifestations including but not limited to those of the cardiovascular, respiratory, neuropsychiatric, gastrointestinal, reproductive, and musculoskeletal systems may persist long after the acute phase. These persistent manifestations, also referred to as long COVID, could impact all patients with COVID-19 across the full spectrum of illness severity. Herein, we comprehensively review the current literature on long COVID, highlighting its epidemiological understanding, the impact of vaccinations, organ-specific sequelae, pathophysiological mechanisms, and multidisciplinary management strategies. In addition, the impact of psychological and psychosomatic factors is also underscored. Despite these crucial findings on long COVID, the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate, and well-designed clinical trials should be prioritized to validate existing hypotheses. Thus, we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.

Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology.

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.

Circulating tumor DNA as liquid biopsy in lung cancer: Biological characteristics and clinical integration.

Lung cancer maintains high morbidity and mortality rate globally despite significant advancements in diagnosis and treatment in the era of precision medicine. Pathological analysis of tumor tissue, the current gold standard for lung cancer diagnosis, is intrusive and intrinsically confined to evaluating the limited amount of tissues that could be physically extracted. However, tissue biopsy has several limitations, including the invasiveness of the procedure and difficulty in obtaining samples for patients at advanced stages., there Additionally,has been no major breakthrough in tumor biomarkers with high specificity and sensitivity, particularly for early-stage lung cancer. Liquid biopsy has been considered a feasible auxiliary tool for tearly dianosis, evaluating treatment responses and monitoring prognosis of lung cancer. Circulating tumor DNA (ctDNA), an ideal biomarker of liquid biopsy, has emerged as one of the most reliable tools for monitoring tumor processes at molecular levels. Herein, this review focuses on tumor heterogeneity to elucidate the superiority of liquid biopsy and retrospectively discussdeciphersolution. We systematically elaborate ctDNA biological characteristics, introduce methods for ctDNA detection, and discuss the current role of plasma ctDNA in lung cancer management. Finally, we summarize the drawbacks of ctDNA analysis and highlight its potential clinical application in lung cancer.

Common nutritional/inflammatory indicators are not effective tools in predicting the overall survival of patients with small cell lung cancer undergoing first-line chemotherapy.

Objective: Various studies have investigated the predictive significance of numerous peripheral blood biomarkers in patients with small cell lung cancer (SCLC). However, their predictive values have not been validated. This study assessed and evaluated the ability of common nutritional or inflammatory indicators to predict overall survival (OS) in patients with SCLC who received first-line chemotherapy. Methods: Between January 2008 and July 2019, 560 patients with SCLC were enrolled at the Sichuan University West China Hospital. Eleven nutritional or inflammatory indices obtained before chemotherapy were evaluated. The cutoff values of continuous peripheral blood indices were confirmed through maximally selected rank statistics. The relationship of peripheral blood indices with OS was investigated through univariate and multivariate Cox regression analyses. Harrell's concordance (C-index) and time-dependent receiver operating characteristic curve were used to evaluate the performance of these indices. Results: A total of 560 patients with SCLC were enrolled in the study. All the patients received first-line chemotherapy. In the univariate Cox analysis, all indices, except the Naples score, were related to OS. In the multivariate analysis, albumin-globulin ratio was an independent factor linked with prognosis. All indices exhibited poor performance in OS prediction, with the area under the curve ranging from 0.500 to 0.700. The lactic dehydrogenase (LDH) and prognostic nutritional index (PNI) were comparatively superior predictors with C-index of 0.568 and 0.550, respectively. The LDH showed incremental predictive values, whereas the PNI showed diminishing values as survival time prolonged, especially for men or smokers. The LDH with highest sensitivity (0.646) and advanced lung cancer inflammation index (ALI) with highest specificity (0.952) were conducive to identifying death and survival at different time points. Conclusion: Common inflammatory or nutritional biomarkers are only marginally useful in predicting outcomes in patients with SCLC receiving first-line chemotherapy. Among them, LDH, PNI, and ALI are relatively promising biomarkers for prognosis evaluation.

Association Between Daily Internet Use and Incidence of Chronic Diseases Among Older Adults: Prospective Cohort Study.

BACKGROUND: Chronic disease incidence among the elderly is increasing, which is correlated with the acceleration of population aging. Evolving internet technologies may help prevent and provide interventions for chronic diseases in an accelerating aging process. However, the impact of daily internet use on the incidence of chronic diseases is not well understood. OBJECTIVE: This study aims to investigate whether daily internet use by middle-aged and older adults may inhibit or promote the occurrence of chronic diseases. METHODS: We included participants from the China Health and Retirement Longitudinal Study (CHARLS), a longitudinal survey of Chinese residents aged ≥45 years. We assessed 8-year data from wave 1 (June 2011-March 2012) to wave 4 (July-September 2018) in CHARLS. Data from wave 4 were used for a cross-sectional study, and data from all 4 waves were used for a longitudinal study. Self-reported data were used to track variables, including internet use, use frequency, and the incidence of different chronic diseases. Cox proportional hazards modeling was applied in the longitudinal study to examine the relationship between daily internet use and chronic diseases among middle-aged and older adults, while adjusting for sociodemographic characteristics and health behaviors. In addition, longitudinal data were used to analyze internet usage trends, and cross-sectional data were used to analyze the factors influencing internet use. RESULTS: Among the 20,113 participants included in the longitudinal analyses, internet use increased significantly, from 2% to 12.3%, between 2011 and 2018. The adjusted model found statistically significant relationships between daily internet use and a lower incidence of the following chronic diseases: hypertension (hazard ratio [HR] 0.78, 95% CI 0.65-0.95, P=.01), chronic lung disease (HR 0.74, 95% CI 0.57-0.97, P=.03), stroke (HR 0.69, 95% CI 0.50-0.94, P=.02), digestive disease (HR 0.73, 95% CI 0.58-0.91, P=.005), memory-related disorders (HR 0.58, 95% CI 0.37-0.91, P=.02), arthritis or rheumatism (HR 0.60, 95% CI 0.48-0.76, P<.001), asthma (HR 0.52, 95% CI 0.33-0.84, P=.007), depression (HR 0.80, 95% CI 0.71-0.89, P<.001), and vision impairment (HR 0.83, 95% CI 0.74-0.93, P=.004). Moreover, our study also showed that with increasing frequency of internet use, the risk of some chronic diseases decreases. CONCLUSIONS: This study found that middle-aged and older adults who use the internet have a reduced risk of developing chronic diseases versus those who do not use the internet. The increasing prevalence of daily internet use among middle-aged and older adults may stimulate contemplation of the potential role of internet platforms in future research on chronic disease prevention.

Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases.

BACKGROUND: Lung cancer prevails and induces high mortality around the world. This study provided real-world information on the evolution of clinicopathological profiles and survival outcomes of lung cancer, and provided survival information within stage I subtypes. METHODS: Patients pathologically confirmed with lung cancer between January 2009 and December 2018 were identified with complete clinicopathological information, molecular testing results, and follow-up data. Shifts in clinical characteristics were evaluated using χ2 tests. Overall survival (OS) was calculated through the Kaplan-Meier method. RESULTS: A total of 26,226 eligible lung cancer patients were included, among whom 62.55% were male and 52.89% were smokers. Non-smokers and elderly patients took increasingly larger proportions in the whole patient population. The proportion of adenocarcinoma increased from 51.63% to 71.80%, while that of squamous carcinoma decreased from 28.43% to 17.60%. Gene mutations including EGFR (52.14%), KRAS (12.14%), and ALK (8.12%) were observed. Female, younger, non-smoking, adenocarcinoma patients and those with mutated EGFR had better survival prognoses. Importantly, this study validated that early detection of early-stage lung cancer patients had contributed to pronounced survival benefits during the decade. Patients with stage I lung cancer, accounted for an increasingly considerable proportion, increasing from 15.28% to 40.25%, coinciding with the surgery rate increasing from 38.14% to 54.25%. Overall, period survival analyses found that 42.69% of patients survived 5 years, and stage I patients had a 5-year OS of 84.20%. Compared with that in 2009-2013, the prognosis of stage I patients in 2014-2018 was dramatically better, with 5-year OS increasing from 73.26% to 87.68%. Regarding the specific survival benefits among stage I patients, the 5-year survival rates were 95.28%, 93.25%, 82.08%, and 74.50% for stage IA1, IA2, IA3, and IB, respectively, far more promising than previous reports. CONCLUSIONS: Crucial clinical and pathological changes have been observed in the past decade. Notably, the increased incidence of stage I lung cancer coincided with an improved prognosis, indicating actual benefits of early detection and management of lung cancer.

Transient receptor potential ankyrin 1 (TRPA1) modulators: Recent update and future perspective.

The transient receptor potential ankyrin 1 (TRPA1) channel is a non-selective cation channel that senses irritant chemicals. Its activation is closely associated with pain, inflammation, and pruritus. TRPA1 antagonists are promising treatments for these diseases, and there has been a recent upsurge in their application to new areas such as cancer, asthma, and Alzheimer's disease. However, due to the generally disappointing performance of TRPA1 antagonists in clinical studies, scientists must pursue the development of antagonists with higher selectivity, metabolic stability, and solubility. Moreover, TRPA1 agonists provide a deeper understanding of activation mechanisms and aid in antagonist screening. Therefore, we summarize the TRPA1 antagonists and agonists developed in recent years, with a particular focus on structure-activity relationships (SARs) and pharmacological activity. In this perspective, we endeavor to keep abreast of cutting-edge ideas and provide inspiration for the development of more effective TRPA1-modulating drugs.

A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics.

During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process.

Distinct immune microenvironment of lung adenocarcinoma in never-smokers from smokers.

Lung cancer in never-smokers (LCINS) presents clinicopathological and molecular features distinct from that in smokers. Tumor microenvironment (TME) plays important roles in cancer progression and therapeutic response. To decipher the difference in TME between never-smoker and smoker lung cancers, we conduct single-cell RNA sequencing on 165,753 cells from 22 treatment-naive lung adenocarcinoma (LUAD) patients. We find that the dysfunction of alveolar cells induced by cigarette smoking contributes more to the aggressiveness of smoker LUADs, while the immunosuppressive microenvironment exerts more effects on never-smoker LUADs' aggressiveness. Moreover, the SPP1hi pro macrophage is identified to be another independent source of monocyte-derived macrophage. Importantly, higher expression of immune checkpoint CD47 and lower expression of major histocompatibility complex (MHC)-I in cancer cells of never-smoker LUADs imply that CD47 may be a better immunotherapy target for LCINS. Therefore, this study reveals the difference of tumorigenesis between never-smoker and smoker LUADs and provides a potential immunotherapy strategy for LCINS.

Impact of the COVID-19 pandemic on cancer healthcare utilization in southwestern China on March 2021.

BACKGROUND: Oncological care has been disrupted worldwide during the COVID-19 pandemic. We aimed to quantify the long-term impact of the pandemic on cancer care utilization and to examine how this impact varied by sociodemographic and clinical factors in southwestern China, where the Dynamic Zero-COVID Strategy was implemented. This strategy mainly included lockdowns, stringent testing, and travel restrictions to prevent the spread of COVID-19. METHOD: We identified 859,497 episodes of the utilization of cancer care from electronic medical records between January 1, 2019, and March 31, 2021, from the cancer center of a tertiary hospital serving an estimated population of 8.4 million in southwestern China. Changes in weekly utilization were evaluated via segmented Poisson regression across service categories, stratified by cancer type and sociodemographic factors. RESULTS: A sharp reduction in utilization of in-person cancer services occurred during the first week of the pandemic outbreak in January 2020, followed by a quick rebound in February 2020. Although there were few COVID-19 cases from March 2020 until this analysis, the recovery of most in-person services was slow and remained incomplete as of March 31, 2021. The exceptions were outpatient radiation and surgery, which increased and exceeded pre-pandemic levels, particularly among lung cancer patients; meanwhile, telemedicine utilization increased substantially after the onset of the pandemic. Care disruptions were most prominent for women, rural residents, uninsured, and breast cancer patients. CONCLUSIONS: As of March 2021, despite few COVID-19 cases, the COVID-19 pandemic has had a strong and continuing impact on in-person oncology care utilization in southwestern China under the Dynamic Zero-COVID Strategy. Equitable and timely access to cancer care requires adjustment in strict policies for COVID-19 prevention and control, as well as targeted remedies for the most vulnerable populations during and beyond the pandemic. Future studies should monitor the long-term effects of the COVID-19 pandemic and response strategies on cancer care and outcomes.

Epigenetic regulation of stem cells in lung cancer oncogenesis and therapy resistance.

Epigenetics plays an important role in regulating stem cell signaling, as well as in the oncogenesis of lung cancer and therapeutic resistance. Determining how to employ these regulatory mechanisms to treat cancer is an intriguing medical challenge. Lung cancer is caused by signals that cause aberrant differentiation of stem cells or progenitor cells. The different pathological subtypes of lung cancer are determined by the cells of origin. Additionally, emerging studies have demonstrated that the occurrence of cancer treatment resistance is connected to the hijacking of normal stem cell capability by lung cancer stem cells, especially in the processes of drug transport, DNA damage repair, and niche protection. In this review, we summarize the principles of the epigenetic regulation of stem cell signaling in relation to the emergence of lung cancer and resistance to therapy. Furthermore, several investigations have shown that the tumor immune microenvironment in lung cancer affects these regulatory pathways. And ongoing experiments on epigenetics-related therapeutic strategies provide new insight for the treatment of lung cancer in the future.

RSK inhibitors as potential anticancer agents: Discovery, optimization, and challenges.

Ribosomal S6 kinase (RSK) family is a group of serine/threonine kinases, including four isoforms (RSK1/2/3/4). As a downstream effector of the Ras-mitogen-activated protein kinase (Ras-MAPK) pathway, RSK participates in many physiological activities such as cell growth, proliferation, and migration, and is intimately involved in tumor occurrence and development. As a result, it is recognized as a potential target for anti-cancer and anti-resistance therapies. There have been several RSK inhibitors discovered or designed in recent decades, but only two have entered clinical trials. Low specificity, low selectivity, and poor pharmacokinetic properties in vivo limit their clinical translation. Published studies performed structure optimization by increasing interaction with RSK, avoiding hydrolysis of pharmacophores, eliminating chirality, adapting to binding site shape, and becoming prodrugs. Besides enhancing efficacy, the focus of further design will move towards selectivity since there are functional differences among RSK isoforms. This review summarized the types of cancers associated with RSK, along with the structural characteristics and optimization process of the reported RSK inhibitors. Furthermore, we addressed the importance of RSK inhibitors' selectivity and discussed future drug development directions. This review is expected to shed light on the emergence of RSK inhibitors with high potency, specificity, and selectivity.

From tuberculosis bedside to bench: UBE2B splicing as a potential biomarker and its regulatory mechanism.

Alternative splicing (AS) is an important approach for pathogens and hosts to remodel transcriptome. However, tuberculosis (TB)-related AS has not been sufficiently explored. Here we presented the first landscape of TB-related AS by long-read sequencing, and screened four AS events (S100A8-intron1-retention intron, RPS20-exon1-alternaitve promoter, KIF13B-exon4-skipping exon (SE) and UBE2B-exon7-SE) as potential biomarkers in an in-house cohort-1. The validations in an in-house cohort-2 (2274 samples) and public datasets (1557 samples) indicated that the latter three AS events are potential promising biomarkers for TB diagnosis, but not for TB progression and prognosis. The excellent performance of classifiers further underscored the diagnostic value of these three biomarkers. Subgroup analyses indicated that UBE2B-exon7-SE splicing was not affected by confounding factors and thus had relatively stable performance. The splicing of UBE2B-exon7-SE can be changed by heat-killed mycobacterium tuberculosis through inhibiting SRSF1 expression. After heat-killed mycobacterium tuberculosis stimulation, 231 ubiquitination proteins in macrophages were differentially expressed, and most of them are apoptosis-related proteins. Taken together, we depicted a global TB-associated splicing profile, developed TB-related AS biomarkers, demonstrated an optimal application scope of target biomarkers and preliminarily elucidated mycobacterium tuberculosis-host interaction from the perspective of splicing, offering a novel insight into the pathophysiology of TB.

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology.

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

Oxygen-carrying sequential preservation mitigates liver grafts ischemia-reperfusion injury.

Oxygen-dependent preservation has been proposed to protect liver grafts from ischemia-reperfusion injury (IRI), but its underlying mechanism remains elusive. Here, we proposed an oxygen-carrying sequential preservation (OCSP) method that combined oxygenated static cold storage (SCS) and normothermic mechanical perfusion. We demonstrated that OCSP, especially with high oxygen partial pressure level (500-650mmHg) during the oxygenated SCS phase, was associated with decreased IRI of liver grafts and improved rat survival after transplantation. A negative correlation between autophagy and endoplasmic reticulum stress response (ERSR) was found under OCSP and functional studies indicated OCSP suppressed ERSR-mediated cell apoptosis through autophagy activation. Further data showed that OCSP-induced autophagy activation and ERSR inhibition were oxygen-dependent. Finally, activated NFE2L2-HMOX1 signaling was found to induce autophagy under OCSP. Together, our findings indicate oxygen-dependent autophagy mitigates liver graft's IRI by ERSR suppression and modulates NFE2L2-HMOX1 signaling under OCSP, providing a theoretical basis for liver preservation using a composite-sequential mode.