Amphiphilic small molecule antimicrobials: From cationic antimicrobial peptides (CAMPs) to mechanism-related, structurally-diverse antimicrobials.

Bacterial infections are characterized by their rapid and widespread proliferation, leading to significant morbidity. Despite the availability of a variety of antimicrobial drugs, the resistance exhibited by pathogenic microorganisms towards these drugs demonstrates a consistent upward trajectory year after year. This trend can be attributed to the abuse or misuse of antibiotics.

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.

Targeting HSP90 for Cancer Therapy: Current Progress and Emerging Prospects.

Heat shock protein 90 (HSP90), a highly conserved member of the heat shock protein family, regulates various proteins and signaling pathways involved in cancer, making it a promising target for cancer therapy. Traditional HSP90 inhibitors have demonstrated significant antitumor potential in preclinical trials, with over 20 compounds advancing to clinical trials and showing promising results. However, the limited clinical efficacy and shared toxicity of these inhibitors restrict their further clinical use.

An update on small molecule compounds targeting synthetic lethality for cancer therapy.

Targeting cancer-specific vulnerabilities through synthetic lethality (SL) is an emerging paradigm in precision oncology. A SL strategy based on PARP inhibitors has demonstrated clinical efficacy. Advances in DNA damage response (DDR) uncover novel SL gene pairs.

Novel tools for early diagnosis and precision treatment based on artificial intelligence.

Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate. Chest computed tomography (CT) serves as an essential tool for lung cancer screening, and pathology images are the gold standard for lung cancer diagnosis.

Single-cell RNA sequencing in tuberculosis: Application and future perspectives.

Tuberculosis (TB) has one of the highest mortality rates among infectious diseases worldwide. The immune response in the host after infection is proposed to contribute significantly to the progression of TB, but the specific mechanisms involved remain to be elucidated. Single-cell RNA sequencing (scRNA-seq) provides unbiased transcriptome sequencing of large quantities of individual cells, thereby defining biological comprehension of cellular heterogeneity and dynamic transcriptome state of cell populations in the field of immunology and is therefore increasingly applied to lung disease research.

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections.

Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide. Accurate diagnosis and early intervention are crucial to improve patient outcomes. Artificial intelligence (AI) has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis, enabling more precise investigation and individualized clinical management.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.