Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

The Relationship Between Static Characteristics of Physicians and Patient Consultation Volume in Internet Hospitals: Quantitative Analysis.

BACKGROUND: Internet medical treatment, also known as telemedicine, represents a paradigm shift in health care delivery. This contactless model allows patients to seek medical advice remotely, often before they physically visit a doctor's clinic. Herein, physicians are in a relatively passive position, as patients browse and choose their health care providers. Although a wealth of experience is undoubtedly a draw for many patients, it remains unclear which specific facets of a doctor's credentials and accomplishments patients prioritize during their selection process. OBJECTIVE: Our primary aim is to delve deeper into the correlation between physicians' static characteristics-such as their qualifications, experiences, and profiles on the internet-and the number of patient visits they receive. We seek to achieve this by analyzing comprehensive internet hospital data from public hospitals. Furthermore, we aim to offer insights into how doctors can present themselves more effectively on web-based platforms, thereby attracting more patients and improving overall patient satisfaction. METHODS: We retrospectively gathered web-based diagnosis and treatment data from the First Affiliated Hospital of Guangxi Medical University in 2023. These data underwent rigorous analysis, encompassing basic descriptive statistics, correlation analyses between key factors in doctors' internet-based introductions, and the number of patient consultation visits. Additionally, we conducted subgroup analyses to ascertain the independence of these vital factors. To further distill the essence from these data, we used nonnegative matrix factorization to identify crucial demographic characteristics that significantly impact patient choice. RESULTS: The statistical results suggested that there were significant differences in the distribution of consultation volume (P<.001), and the correlation analysis results suggested that there was a strong correlation between the two groups of data (ρ=0.93; P<.001). There was a correlation between the richness of a profile and popularity (P<.001). Patients were more interested in physicians with advanced titles, doctoral degrees, social activities, and scientific achievements (P<.001) as well as other institutional visit experiences (P=.003). More prosperous social activities, scientific achievements, experiences of other institutional visits, and awards were more common among people with advanced professional titles. Doctoral degrees remained attractive to patients when data were limited to senior physicians (P<.001). Patients trusted the medical staff with advanced titles, social activities, scientific achievements, and doctoral degrees (P<.001). CONCLUSIONS: Patient preferences for choosing a health care provider differed significantly between free and paid consultations. Notably, patients tended to trust doctors with advanced professional titles more and were more likely to seek out those with doctoral qualifications over other professional ranks. Additionally, physicians who actively participated in social events and scientific endeavors often had an advantage in attracting new patients. Given these insights, doctors who invest in enhancing their personal and professional experiences within these domains are likely to see increased popularity and patient satisfaction.

Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.

BACKGROUND: Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. METHODS: Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. RESULTS: A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. CONCLUSION: The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

Auxin efflux carrier PsPIN4 identified through genome-wide analysis as vital factor of petal abscission.

PIN-FORMED (PIN) proteins, which function as efflux transporters, play many crucial roles in the polar transportation of auxin within plants. In this study, the exogenous applications of auxin IAA and TIBA were found to significantly prolong and shorten the florescence of tree peony (Paeonia suffruticosa Andr.) flowers. This finding suggests that auxin has some regulatory influence in petal senescence and abscission. Further analysis revealed a total of 8 PsPINs distributed across three chromosomes, which could be categorized into two classes based on phylogenetic and structural analysis. PsPIN1, PsPIN2a-b, and PsPIN4 were separated into the "long" PIN category, while PsPIN5, PsPIN6a-b, and PsPIN8 belonged to the "short" one. Additionally, the cis-regulatory elements of PsPIN promoters were associated with plant development, phytohormones, and environmental stress. These genes displayed tissue-specific expression, and phosphorylation sites were abundant throughout the protein family. Notably, PsPIN4 displayed distinct and elevated expression levels in roots, leaves, and flower organs. Expression patterns among the abscission zone (AZ) and adjacent areas during various flowering stages and IAA treatment indicate that PsPIN4 likely influences the initiation of peony petal abscission. The PsPIN4 protein was observed to be co-localized on both the plasma membrane and the cell nucleus. The ectopic expression of PsPIN4 reversed the premature flower organs abscission in the Atpin4 and significantly protracted florescence when introduced to Col Arabidopsis. Our findings established a strong basis for further investigation of PIN gene biological functions, particularly concerning intrinsic relationship between PIN-mediated auxin polar.

Integrated neutrophil-to-lymphocyte ratio and handgrip strength better predict survival in patients with cancer cachexia.

OBJECTIVES: Systemic inflammation and skeletal muscle strength play crucial roles in the development and progression of cancer cachexia. In this study we aimed to evaluate the combined prognostic value of neutrophil-to-lymphocyte ratio (NLR) and handgrip strength (HGS) for survival in patients with cancer cachexia. METHODS: This multicenter cohort study involved 1826 patients with cancer cachexia. The NLR-HGS (NH) index was defined as the ratio of neutrophil-to-lymphocyte ratio to handgrip strength. Harrell's C index and receiver operating characteristic (ROC) curve analysis were used to assess the prognosis of NH. Kaplan-Meier analysis and Cox regression models were used to evaluate the association of NH with all-cause mortality. RESULTS: Based on the optimal stratification, 380 women (NH > 0.14) and 249 men (NH > 0.19) were classified as having high NH. NH has shown greater predictive value compared to other indicators in predicting the survival of patients with cancer cachexia according to the 1-, 3-, and 5-y ROC analysis and Harrell's C index calculation. Multivariate survival analysis showed that higher NH was independently associated with an increased risk of death (hazard ratio = 1.654, 95% confidence interval = 1.389-1.969). CONCLUSION: This study demonstrates that the NH index, in combination with NLR and HGS, is an effective predictor of the prognosis of patients with cancer cachexia. It can offer effective prognosis stratification and guidance for their treatment.

Value of the modified Patient-Generated Subjective Global Assessment in indicating the need for nutrition intervention and predicting overall survival in patients with malignant tumors in at least two organs.

BACKGROUND: Although the Patient-Generated Subjective Global Assessment (PG-SGA) is a reference standard used to assess a patient's nutrition status, it is cumbersome to administer. The aim of the present study was to estimate the value of a simpler and easier-to-use modified PG-SGA (mPG-SGA) to evaluate the nutrition status and need for intervention in patients with malignant tumors present in at least two organs. METHODS: A total of 591 patients (343 male and 248 female) were included from the INSCOC study. A Pearson correlation analysis was conducted to assess the correlation between the mPG-SGA and nutrition-related factors, with the optimal cut-off defined by a receiver operating characteristic curve (ROC). The consistency between the mPG-SGA and PG-SGA was compared in a concordance analysis. A survival analysis was used to determine the effects of nutritional intervention among different nutrition status groups. Univariable and multivariable Cox analyses were applied to evaluate the association of the mPG-SGA with the all-cause mortality. RESULTS: The mPG-SGA showed a negative association with nutrition-related factors. Individuals with an mPG-SGA ≥ 5 (rounded from 4.5) were considered to need nutritional intervention. Among the malnourished patients (mPG-SGA ≥ 5), the overall survival (OS) of those who received nutrition intervention was significantly higher than that of patients who did not. However, the OS was not significantly different in the better-nourished patients (mPG-SGA < 5). CONCLUSION: Our findings support that the mPG-SGA is a feasible tool that can be used to guide nutritional interventions and predict the survival of patients with malignant tumors affecting at least two organs.

X-ray Activated Nanoprodrug for Visualization of Cortical Microvascular Alterations and NIR-II Image-Guided Chemo-Radiotherapy of Glioblastoma.

The permeability of the highly selective blood-brain barrier (BBB) to anticancer drugs and the difficulties in defining deep tumor boundaries often reduce the effectiveness of glioma treatment. Thus, exploring the combination of multiple treatment modalities under the guidance of second-generation near-infrared (NIR-II) window fluorescence (FL) imaging is considered a strategic approach in glioma theranostics. Herein, a hybrid X-ray-activated nanoprodrug was developed to precisely visualize the structural features of glioma microvasculature and delineate the boundary of glioma for synergistic chemo-radiotherapy. The nanoprodrug comprised down-converted nanoparticle (DCNP) coated with X-ray sensitive poly(Se-Se/DOX-co-acrylic acid) and targeted Angiopep-2 peptide (DCNP@P(Se-DOX)@ANG). Because of its ultrasmall size and the presence of DOX, the nanoprodrug could easily cross BBB to precisely monitor and localize glioblastoma via intracranial NIR-II FL imaging and synergistically administer antiglioblastoma chemo-radiotherapy through specific X-ray-induced DOX release and radiosensitization. This study provides a novel and effective strategy for glioblastoma imaging and chemo-radiotherapy.

Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data.

OBJECTIVE: The aim of this study is using clinical factors and non-enhanced computed tomography (CT) deep features of the psoas muscles at third lumbar vertebral (L3) level to construct a model to predict malnutrition in gastric cancer before surgery, and to provide a new nutritional status assessment and survival assessment tool for gastric cancer patients. METHODS: A retrospective analysis of 312 patients of gastric cancer were divided into malnutrition group and normal group based on Nutrition Risk Screening 2002(NRS-2002). 312 regions of interest (ROI) of the psoas muscles at L3 level of non-enhanced CT were delineated. Deep learning (DL) features were extracted from the ROI using a deep migration model and were screened by principal component analysis (PCA) and least-squares operator (LASSO). The clinical predictors included Body Mass Index (BMI), lymphocyte and albumin. Both deep learning model (including deep learning features) and mixed model (including selected deep learning features and selected clinical predictors) were constructed by 11 classifiers. The model was evaluated and selected by calculating receiver operating characteristic (ROC), area under curve (AUC), accuracy, sensitivity and specificity, calibration curve and decision curve analysis (DCA). The Cohen's Kappa coefficient (κ) was using to compare the diagnostic agreement for malnutrition between the mixed model and the GLIM in gastric cancer patients. RESULT: The results of logistics multivariate analysis showed that BMI [OR = 0.569 (95% CI 0.491-0.660)], lymphocyte [OR = 0.638 (95% CI 0.408-0.998)], and albumin [OR = 0.924 (95% CI 0.859-0.994)] were clinically independent malnutrition of gastric cancer predictor(P < 0.05). Among the 11 classifiers, the Multilayer Perceptron (MLP)were selected as the best classifier. The AUC of the training and test sets for deep learning model were 0.806 (95% CI 0.7485-0.8635) and 0.769 (95% CI 0.673-0.863) and with accuracies were 0.734 and 0.766, respectively. The AUC of the training and test sets for the mixed model were 0.909 (95% CI 0.869-0.948) and 0.857 (95% CI 0.782-0.931) and with accuracies of 0.845 and 0.861, respectively. The DCA confirmed the clinical benefit of the both models. The Cohen's Kappa coefficient (κ) was 0.647 (P < 0.001). Diagnostic agreement for malnutrition between the mixed model and GLIM criteria was good. The mixed model was used to calculate the predicted probability of malnutrition in gastric cancer patients, which was divided into high-risk and low-risk groups by median, and the survival analysis showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group (P = 0.005). CONCLUSION: Deep learning based on mixed model may be a potential tool for predicting malnutrition in gastric cancer patients.

Application of the quality of recovery-40 questionnaire to evaluate the effectiveness of enhanced recovery after surgery protocols in gastric cancer.

Patient reported outcomes is currently considered to be an important supplement to evaluate the effectiveness of enhanced recovery after surgery (ERAS) clinical practice. The Quality of Recovery-40 Questionnaire (QoR-40) is one of the most frequently used and validation tool to assess the subjective feelings of quality of life after surgery. The present study aimed to use the QoR-40 to evaluate the effectiveness of ERAS protocols in gastric cancer from the perspective of patient-reported quality of recovery. The study was designed as a prospective, non-randomized clinical trial, conducted in a single center. Patients in our hospital who were scheduled to undergo radical surgery for gastric cancer were divided into ERAS group and control group (Contr group). The QoR-40 were administered one day before surgery (Baseline) and on postoperative day 1, 3, 6, and 30. The difference in QoR-40 scores between the ERAS and Contr groups was compared by repeated-measures ANOVA. A total of 200 patients completed the study, including 100 patients in the ERAS group and 100 patients in the Contr group. The Baseline time point QoR-40 scores of the ERAS and Contr groups were 179.68 ± 14.46 and 180.12 ± 17.12, respectively, and no significant difference was noted between the two groups (p = 0.845). The postoperative QoR-40 score of the ERAS group was significantly higher than that of the Contr group, and the difference was statistically significant (p = 0.006). This study demonstrated that, in terms of patient-reported quality of recovery, the postoperative recovery effect of ERAS protocols in gastric cancer is significantly better than that of the traditional treatment model.

Prevalence and unfavorable outcome of frailty in older adults with gastric cancer: a systematic review and meta-analysis.

BACKGROUND AND AIM: Previous studies reported inconsistent results on the prevalence and prognostic implications of frailty among older adults with gastric cancer. This systematic review synthesized available literature pertaining on this topic to establish the prevalence and unfavorable outcomes of frailty in older adults with gastric cancer. METHODS: A comprehensive search was conducted across multiple English databases including PubMed, Cochrane Library, CINAHL, Embase, and Web of Science as well as Chinese databases, namely, CNKI, Wan Fang, and CBM, from inception to July 4, 2023, to identify potential studies. Data related to the incidence of frailty and its unfavorable outcomes in older adults with gastric cancer were extracted. RevMan5.3 and R 4.2.2 were used to evaluate pooled prevalence, hazard ratios (HR), and 95% confidence interval (CI). RESULTS: This review comprehensively selected 13 studies, comprising 9 cohort studies and 4 cross-sectional studies, on 44,117 older adults diagnosed with gastric cancer. The incidence of frailty among older adults with gastric cancer ranged from 10 to 71%. The pooled prevalence of frailty was 29% (95% CI 0.21-0.39). Frailty was found to be associated with an elevated risk of postoperative complications (HR = 1.99, 95% CI 1.45-2.73), prolonged postoperative hospital stay (HR = 2.68, 95% CI 2.38-3.02), likelihood of readmission (HR = 3.28, 95% CI 1.77-6.08), and an increased mortality risk (HR = 1.60, 95% CI 1.36-1.90). CONCLUSIONS: Frailty was associated with a poor prognosis in older adults with gastric cancer. Clinical medical staff should focus on the frailty of older adults with gastric cancer, conduct large-scale, multicenter, and prospective studies and early screening of patients, and provide guidance for the implementation of prevention and treatment strategies.

Identification of CERS5 as a molecular biomarker in pan-cancer through multiple omics integrative analysis.

Cancer is a devastating disease that presents a major threat to human health. The protein CERS5 is responsible for synthesizing C16-ceramide, but its role in cancer is poorly understood. In this study, we examined the connection between CERS5 expression and pan-cancer prognosis, diagnosis, and the molecular mechanism involved. Kaplan-Meier survival analysis revealed variations among different cancer types. Functional enrichment analysis was conducted using gene set enrichment analysis (GSEA), and a network of protein-protein interaction (PPI) was constructed. The relationship between CERS5 and 22 immune infiltrating cell categories was detected using CIBERSORT. Single-cell analysis revealed elevated CERS5 levels in fibroblasts, which are vital in tumor immunity. The relationship between the expression of CERS5 and the immune-related genes, microsatellite instability, tumor mutational burden, and RNA modification genes in cancer were examined using the pan-cancer database. The role of CERS5 in immune regulation might be crucial to the tumor microenvironment. Pathway enrichment analysis indicated associations between CERS5 and extracellular matrix-receptor interaction, the WNT signaling pathway, and cell-cell junctions. Specifically, CERS5 was positively correlated with Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), Programmed Cell Death 1 (PDCD1), and Lymphocyte Activating 3 (LAG3) in stomach adenocarcinoma. In vitro, knockdown of CERS5 significantly hindered gastric cancer cells' ability to proliferate, migrate invade and increased apoptotic rate. We believe that CERS5 could be a promising target for future cancer research, contributing to the development of effective therapies.

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.

CLIC1-mediated autophagy confers resistance to DDP in gastric cancer.

Gastric cancer has been a constant concern to researchers as one of the most common malignant tumors worldwide. The treatment options for gastric cancer include surgery, chemotherapy and traditional Chinese medicine. Chemotherapy is an effective treatment for patients with advanced gastric cancer. Cisplatin (DDP) has been approved as a critical chemotherapy drug to treat various kinds of solid tumors. Although DDP is an effective chemotherapeutic agent, many patients develop drug resistance during treatment, which has become a severe problem in clinical chemotherapy. This study aims to investigate the mechanism of DDP resistance in gastric cancer. The results show that intracellular chloride channel 1 (CLIC1) expression was increased in AGS/DDP and MKN28/DDP, and as compared to the parental cells, autophagy was activated. In addition, the sensitivity of gastric cancer cells to DDP was decreased compared to the control group, and autophagy increased after overexpression of CLIC1. On the contrary, gastric cancer cells were more sensitive to cisplatin after transfection of CLIC1siRNA or treatment with autophagy inhibitors. These experiments suggest that CLIC1 could alter the sensitivity of gastric cancer cells to DDP by activating autophagy. Overall, the results of this study recommend a novel mechanism of DDP resistance in gastric cancer.

Systemic therapy with or without local intervention for oligometastatic oesophageal squamous cell carcinoma (ESO-Shanghai 13): an open-label, randomised, phase 2 trial.

BACKGROUND: The efficacy of local therapy for patients with oligometastatic oesophageal squamous cell carcinoma is unclear. We aimed to assess the efficacy of local plus systemic therapy compared with systemic therapy alone in patients with oligometastatic oesophageal squamous cell carcinoma. METHODS: The ESO-Shanghai 13 trial was a randomised, open-label, multicentre, phase 2 trial. Patients (aged ≥18 years) were recruited from six hospitals in China with histological confirmation of oligometastatic oesophageal squamous cell carcinoma with a controlled primary tumour and one to four metastatic lesions. Eligible patients were randomly assigned via a computer-generated schedule in a 1:1 ratio to receive either systemic therapy alone (ie, systemic therapy only group) or combined systemic and local therapy (ie, systemic and local therapy group). The systemic therapy regimens in both groups were at the discretion of the investigator and included chemotherapy alone, anti-PD-1 antibodies alone, or chemotherapy plus anti-PD-1 antibodies. Local therapy-radiotherapy, surgery, or thermal ablation-was delivered to all metastatic lesions for patients in the systemic and local therapy group. Randomisation was balanced dynamically on three factors: the number of disease sites, the lines of systemic therapy, and the location of the metastases. Patients and investigators were not masked to treatment allocation. The primary endpoint was progression-free survival, defined as the time from randomisation to progression or death from any cause in the intention-to-treat population. The safety population included all patients who had undergone random assignment and at least one of the intended therapies. This trial is registered with ClinicalTrials.gov, NCT03904927. The trial is ongoing but closed to new participants. FINDINGS: 116 patients were screened for enrolment between March 5, 2019, and Sept 16, 2021, and 104 patients who met the eligibility criteria were randomly assigned to the systemic and local therapy group (n=53) or the systemic therapy only group (n=51). 20 (38%) patients in the systemic plus local therapy group and 23 (45%) patients in the systemic therapy only group received anti-PD-1 antibody-based systemic therapy; three patients in the systemic and local therapy group did not receive systemic therapy. At a median follow-up of 30·5 months (IQR 24·7-37·8), median progression-free survival was 15·3 months (95% CI 10·1-20·5) in the systemic and local therapy group versus 6·4 months (5·2-7·6) in the systemic therapy only group (stratified hazard ratio 0·26 [95% CI 0·16-0·42]; stratified log rank p<0·0001). Grade 1-2 acute oesophagitis was more common in the systemic and local therapy group than in the systemic therapy only group (10 [19%] vs one [2%] patients; p=0·036). The number of patients who had grade 3 or worse treatment-related adverse events was similar between groups (25 [47%] vs 21 [41%]; p=0·538), with the most common adverse events being leukocytopenia (17 [32%] vs 18 [35%]) and neutropenia (19 [36%] vs 20 [39%]). Treatment-related deaths occurred in two patients in the systemic and local therapy group and one patient in the systemic therapy only group. INTERPRETATION: The addition of local treatment for metastases could significantly improve progression-free survival among patients with oligometastatic oesophageal squamous cell carcinoma being treated with systemic therapy. Our findings suggest that combining local and systemic therapy could be a treatment option for patients with oligometastatic oesophageal squamous cell carcinoma, but further support from phase 3 trials is required. FUNDING: Science and Technology Commission of Shanghai Municipality, National Nature Science Foundation of China, and Shanghai Municipal Health Commission. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Occurrence and characterization of NDM-5-producing Escherichia coli from retail eggs.

The New Delhi Metallo-ß-lactamase (NDM) producing Enterobacterales has been detected from diverse sources but has rarely been reported in retail eggs. In this study, 144 eggshell and 96 egg content samples were collected in 2022 from Guangdong province and were screened for NDM-producing strains. Four Escherichia coli strains (ST3014, ST10, ST1485, and ST14747) recovered from two (1.39%, 2 of 144) eggshells and two (2.08%, 2 of 96) egg content samples were identified as blaNDM-5-positive strains. Oxford Nanopore MinION sequencing and conjugation assays revealed that the blaNDM-5 gene was carried by IncX3 (n = 1), IncI1 (n = 1), and IncHI2 (n = 2). The IncI1-plasmid-carrying blaNDM-5 displayed high homology with one plasmid pEC6563-NDM5 from the human clinic, while the IncHI2 plasmid harboring blaNDM-5 shared highly similar structures with plasmids of animal origin. To the best of our knowledge, this is the first report on the identification of blaNDM-5-positive bacteria in retail eggs. NDM-producing E. coli could be transmitted to humans by the consumption of eggs or direct contact, which could pose a potential threat to human health.

Investigating the prognostic and predictive value of the type II cystatin genes in gastric cancer.

BACKGROUND: Accumulating evidence indicates that type II cystatin (CST) genes play a pivotal role in several tumor pathological processes, thereby affecting all stages of tumorigenesis and tumor development. However, the prognostic and predictive value of type II CST genes in GC has not yet been investigated. METHODS: The present study evaluated the expression and prognostic value of type II CST genes in GC by using The Cancer Genome Atlas (TCGA) database and the Kaplan-Meier plotter (KM plotter) online database. The type II CST genes related to the prognosis of GC were then screened out. We then validated the expression and prognostic value of these genes by immunohistochemistry. We also used Database for Annotation, Visualization, and Integrated Discovery (DAVID), Gene Multiple Association Network Integration Algorithm (GeneMANIA), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), nomogram, genome-wide co-expression analysis, and other bioinformatics tools to analyze the value of type II CST genes in GC and the underlying mechanism. RESULTS: The data from the TCGA database and the KM plotter online database showed that high expression of CST2 and CST4 was associated with the overall survival (OS) of patients with GC. The immunohistochemical expression analysis showed that patients with high expression of CST4 in GC tissues have a shorter OS than those with low expression of CST4 (HR = 1.85,95%CI: 1.13-3.03, P = 0.015). Multivariate Cox regression analysis confirmed that the high expression level of CST4 was an independent prognostic risk factor for OS. CONCLUSIONS: Our findings suggest that CST4 could serve as a tumor marker that affects the prognosis of GC and could be considered as a potential therapeutic target for GC.

Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a highly lethal form of cancer. Cuproptosis is a recently discovered form of regulated cell death. However, its significance in ESCC remains largely unknown. In this study, we observed significant expression differences in most of the 12 cuproptosis-related genes (CRGs) in the TCGA-ESCC dataset, which was validated using GSE20347, GSE38129, and individual ESCC datasets. We were able to divide patients in the TCGA-ESCC cohort into two subgroups based on disease, and found significant differences in survivor outcomes and biological functions between these subgroups. Additionally, we identified 11 prognosis-related genes from the 12 CRGs using LASSO COX regression analysis and constructed a CRGs signature for ESCC. Patients were categorized into high- and low-risk subgroups based on their median risk score, with those in the high-risk subgroup having significantly worse overall survival than those in the low-risk subgroup. The CRGs signature was also highly accurate in predicting prognosis and survival outcomes. Univariate and multivariate Cox regression analyses revealed that 8 of the 11 CRGs were independent prognostic factors for predicting survival in ESCC patients. Furthermore, our nomogram performed well and could serve as a useful tool for predicting prognosis. Finally, our risk model was found to be relevant to the sensitivity of targeted agents and immune infiltration. Functional enrichment analysis demonstrated that the risk model was associated with biological pathways of tumor migration and invasion. In summary, our study may provide a promising prognostic signature based on CRGs and offers potential targets for personalized therapy.

Predicting Vault and Size of Posterior Chamber Phakic Intraocular Lens Using Sulcus to Sulcus-Optimized Artificial Intelligence Technology.

PURPOSE: To investigate the accuracy of posterior chamber phakic intraocular lens (PIOL) vault and size prediction models based on sulcus to sulcus (STS) optimized artificial intelligence and big data analysis technology. DESIGN: Big data and artificial intelligence prediction model. METHODS: We included 5873 eyes with posterior chamber PIOL implantation, and the postoperative vault was measured using an anterior segment analyzer (Pentacam AXL) 1 month postoperatively. A random forest regression model and classification model were used to predict the postoperative vault and PIOL size. The postoperative vault and PIOL size were set as output features; other vault-related eye parameters were set as input features. The influence of white to white (WTW), horizontal sulcus to sulcus (STS), and vertical STS on predicting postoperative vault and PIOL size was analyzed and compared. RESULTS: The mean preoperative WTW diameter was 11.64 ± 0.37 mm, the mean horizontal STS diameter was 11.85 ± 0.47 mm, and the mean vertical STS diameter was 12.39 ± 0.52 mm. In the regression model for numerical prediction of the vault, the combination of WTW, horizontal STS, and vertical STS was the most optimal for vault prediction (R2 = 0.3091, root mean square error [RMSE] = 0.1705); solely relying on WTW was the least optimal (R2 = 0.2849, RMSE = 0.1735). Among the models for classification prediction of the vault, the combination of WTW, horizontal STS, and vertical STS was the most accurate (accuracy, 0.6302; mean area under the curve, 0.8008; and mean precision recall rate, 0.6940). Moreover, the combination of WTW, horizontal STS, and vertical STS exhibited the highest accuracy for classification prediction of PIOL size (accuracy, 0.8170; mean area under the curve, 0.9540; and mean precision recall rate, 0.8864). Whether in the regression prediction models of vault values or in the classification prediction models of vault and PIOL size, the accuracy of STS optimized model was significantly improved compared with the traditional WTW model (P < .001). CONCLUSION: Artificial intelligence combined with STS optimization contributes to the accuracy of PIOL size and vault prediction models. The random forest machine-learning model optimized by STS is superior to the traditional WTW model.

The Prognostic Significance of Nomogram-Based Pretreatment Inflammatory Indicators in Patients With Esophageal Squamous Cell Carcinoma Receiving Intensity-Modulated Radiotherapy.

BACKGROUND: At present, there is no objective prognostic index available for patients with esophageal squamous cell carcinoma (ESCC) who underwent intensity-modulated radiotherapy (IMRT). This study is to develop a nomogram based on hematologic inflammatory indices for ESCC patients treated with IMRT. METHODS: 581 patients with ESCC receiving definitive IMRT were enrolled in our retrospective study. Of which, 434 patients with treatment-naïve ESCC in Fujian Cancer Hospital were defined as the training cohort. Additional 147 newly diagnosed ESCC patients were used as the validation cohort. Independent predictors of overall survival (OS) were employed to establish a nomogram model. The predictive ability was evaluated by time-dependent receiver operating characteristic curves, the concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI). Decision curve analysis (DCA) was performed to assess the clinical benefits of the nomogram model. The entire series was divided into 3 risk subgroups stratified by the total nomogram scores. RESULTS: Clinical TNM staging, primary gross tumor volume, chemotherapy, neutrophil-to-lymphocyte ratio and platelet lymphocyte ratio were independent predictors of OS. Nomogram was developed incorporating these factors. Compared with the 8th American Joint Committee on Cancer (AJCC) staging, the C-index for 5-year OS (.627 and .629) and the AUC value of 5-year OS (.706 and .719) in the training and validation cohorts (respectively) were superior. Furthermore, the nomogram model presented higher NRI and IDI. DCA also demonstrated that the nomogram model provided greater clinical benefit. Finally, patients with <84.8, 84.8-151.4, and >151.4 points were categorized into low-risk, intermediate-risk, and high-risk groups. Their 5-year OS rates were 44.0%, 23.6%, and 8.9%, respectively. The C-index was .625, which was higher than the 8th AJCC staging. CONCLUSIONS: We have developed a nomogram model that enables risk-stratification of patients with ESCC receiving definitive IMRT. Our findings may serve as a reference for personalized treatment.

Sarcopenia prevalence in patients with cancer and association with adverse prognosis: A nationwide survey on common cancers.

OBJECTIVE: Although previous studies have implicated the negative outcomes of sarcopenia, evidence is limited to one or a few types of cancer. The aim of this study was to evaluate the distribution and influencing factors of sarcopenia, and explore the relationship between sarcopenia and cancer prognosis in a large oncological population. METHODS: This observational cohort study included patients diagnosed with malignant cancer between May 2011 and January 2019. Hematologic and anthropometric parameters were collected prospectively. Low skeletal muscle mass and radiodensity were diagnosed using clinical indicators, according to the two prediction models. The importance of potential risk factors for sarcopenia was estimated by subtracting the predicted degrees of freedom from the partial χ2 statistic. Hazard rates of death were calculated using the hazard function and Cox regression analyses. RESULTS: We included 13 761 patients with cancer; the prevalence of sarcopenia was 33%. The median age was 58 y and 7135 patients (52%) were men. Patients with sarcopenia had a worse nutritional status and quality of life than those without sarcopenia. Age was the most important risk factor for sarcopenia compared with body mass index or TNM stage. Additionally, patients with sarcopenia had a significantly higher and earlier peak risk for mortality. After adjusting for baseline characteristics, sarcopenia was independently associated with mortality in the research population (hazard ratio, 1.429; P < 0.001) and most cancer types. CONCLUSION: Age is the most important risk factor for sarcopenia even in patients with cancer. Sarcopenia is strongly associated with a poor quality of life and reduced overall survival.