Magnetism and electronic properties of ConMoP (n = 1 ~ 5) cluster: a DFT study.

CONTEXT: Hydrogen has emerged as a promising clean energy carrier, underscoring the imperative need to comprehend its adsorption mechanisms. This study delves into the magnetic and electronic properties of Co-Mo-P clusters, aiming to unveil their catalytic potential in hydrogen production. Employing density functional theory (DFT), we optimized cluster configurations and scrutinized their magnetic behaviors. Our investigation unveiled 16 stable configurations of the ConMoP (n = 1 ~ 5) cluster, predominantly in steric forms. The magnetic attributes were primarily ascribed to the d orbitals of Co metal atoms, with Co3MoP exhibiting exceptional magnetic characteristics. Analysis of density of state diagrams revealed the prevalence of spin-up α-electrons in d orbitals, while spin-down ß-electrons attenuated overall magnetic properties. Localized orbital (LOL) analysis highlighted stable covalent bonds within the clusters, affirming their catalytic potential. Orbital delocalization index (ODI) analysis revealed diverse spatial distribution ranges for orbitals across different configurations, suggesting a progressive attenuation of off-domain properties with increasing cluster size. Furthermore, infrared spectroscopy unveiled distinct vibrational peaks in various configurations, indicative of unique infrared activities. These findings contribute to a nuanced theoretical understanding of Co-Mo-P clusters and pave the path for future research aimed at augmenting their catalytic efficiency in hydrogen production. This study underscores the viability of Co-Mo-P clusters as alternatives to conventional Pt catalysts, offering insights into the design of novel materials for sustainable energy applications. Further research is warranted to explore the behavior of the Co-Mo-P system under diverse reaction conditions, fostering advancements in materials and energy science. METHODS: In this study, we harnessed the ConMoP (n = 1 ~ 5) cluster as a simulation platform for probing the local structure of the material. Our aim was to scrutinize the magnetism, electronic characteristics influenced by the varying metal atoms within these clusters. A systematic exploration involved incrementing the number of metal atoms and expanding the cluster size to elucidate the corresponding property variations. Density functional theory (DFT) calculations were pivotal to our methodology, employing the B3LYP hybrid functional implemented in the Gaussian 16 software package. The ConMoP (n = 1 ~ 5) cluster underwent optimization calculations and vibrational analysis at the def2-tzvp quantization level, yielding optimized configurations with diverse spin multiplet degrees. To comprehensively characterize and visually represent the stability, electronic features, and catalytic attributes of these configurations, we employed a suite of computational tools. Specifically, quantum chemistry software GaussView and wave function analysis software Multiwfn played integral roles. Through the integrated use of these computational tools, we acquired valuable insights into the magnetism, electronic characteristics of the ConMoP (n = 1 ~ 5) cluster, shedding light on their dependency on distinct metal atoms.

Density functional theory studies on properties of cluster ConMoS (n=1 ~ 5): interatomic interactions, electronic properties, frontier orbitals.

CONTEXT: To comprehend the microscopic property alterations within the ConMoS cluster (n=1-5), this study investigates its internal interactions, electronic characteristics, and orbital correlations employing density functional theory. Structural optimization and theoretical analysis of the cluster are conducted using the Gaussian09 software package, considering various spin multiplicities and employing the B3LYP/def2tzvp quantum chemical method as the computational standard. The outcomes reveal the optimization of the cluster, resulting in 21 stable configurations while continually acquiring energy from the external environment. Analysis of the interaction region indicator functions, the independent gradient model based on Hirshfeld partition, the localized orbital indicator functions, and the electron localization function reveals a trend toward chemical bonding interactions within the interatomic interaction regions. Moreover, the interatomic forces exhibit a high likelihood of engaging in covalent bonding interactions. Both Co and Mo atoms display greater electron delocalization, facilitating the exchange of electrons with the external environment. The paper discuss electron space range, hardness and softness, polarizability, dipole moment, Mulliken population analysis, density of states, HOMO-LUMO diagram, and UV-Vis spectra. Configuration 5a exhibits the broadest electron delocalization and the highest reactivity. It maintains structural stability in external conditions and displays the most polarized molecules. Metal atoms in this cluster exhibit superior mobility compared to non-metal atoms. We elucidate the electron density aggregation region within the cluster. Configuration 1a demonstrates the highest correlation with molar absorption coefficient for its peak. Analyzing the HOMO and LUMO orbital delocalization index and center-of-mass distances revealed that the front orbits of configuration 5a exhibited a broad distribution in space and the minimum center-of-mass distance. METHODS: This study presents a theoretical investigation of Co-Mo-S clusters employing density functional theory (DFT). DFT is a prevalent method for exploring the electronic structure and characteristics of atoms, molecules, and solids. The paper examines cluster attributes encompassing interatomic interactions, electronic properties, and frontier orbitals. Gaussian09 software is employed for optimizing cluster structures, while the analysis is augmented using Multiwfn wave function analysis software. By harnessing these theoretical and computational tools, it aims to delve deeper into cluster properties, yielding valuable insights.

Stability, electronic and catalytic properties of ConMoP(n = 1 ~ 5) clusters: A DFT study.

CONTEXT: The investigation of the stability, electronic properties, and catalytic activity of clusters ConMoP holds significant applications and implications in catalyst design, materials science, energy conversion and storage, and environmental protection. The study aims to delve into the unique features of the clusters ConMoP(n = 1 ~ 5), aiming to drive advancements in these related fields. The results obtained from the analysis revealed the stable configurations of the ten clusters, primarily characterized by steric structures. Furthermore, the energy of the clusters was found to increase continuously during growth, as indicated by calculations of atomic fragmentation energy and atomic binding energy. The researchers conducted an analysis of the Natural Population Analysis(NPA) charge, which revealed that Co atoms acted as electron donors, while P and Mo atoms acted as electron acceptors within the clusters. Additionally, an examination of the electrostatic potential indicated that Co and Mo atoms displayed nucleophilic tendencies, while P atoms exhibited electrophilic characteristics. Moreover, the density of states curves, HOMO and LUMO orbitals, and Kooperman's theorem were applied to the clusters ConMoP(n = 1 ~ 5).Through this study, a deeper understanding of the properties and behavior of clusters ConMoP has been achieved, shedding light on their potential as catalysts. The findings contribute to the existing knowledge of these clusters and provide a basis for further research and exploration in this field. METHODS: In this study, we employed the clusters ConMoP(n = 1 ~ 5) to simulate the local structure of the material, enabling us to investigate the stability, electronic properties, and catalytic properties influenced by the metal atoms. By systematically increasing the number of metal atoms and expanding the cluster size, we explored the variations in these properties. Density functional theory (DFT) calculations were performed using the B3LYP hybrid functional implemented in the Gaussian09 software package. The clusters ConMoP(n = 1 ~ 5) underwent optimization calculations and vibrational analysis at the def2-tzvp quantization level, resulting in optimized configurations with different spin multiplet degrees. For data characterization and graphical representation of the stability, electronic properties, and catalytic properties of the optimized configurations, we utilized a range of computational tools. Specifically, the quantum chemistry software GaussView, wave function analysis software Multiwfn were employed. Through the comprehensive utilization of these computational tools, we gained valuable insights into the stability, electronic properties, and catalytic properties of the clusters ConMoP(n = 1 ~ 5) and their dependence on different metal atoms.

Detection of pancreatic cancer with two- and three-dimensional radiomic analysis in a nationwide population-based real-world dataset.

BACKGROUND: CT is the major detection tool for pancreatic cancer (PC). However, approximately 40% of PCs < 2 cm are missed on CT, underscoring a pressing need for tools to supplement radiologist interpretation. METHODS: Contrast-enhanced CT studies of 546 patients with pancreatic adenocarcinoma diagnosed by histology/cytology between January 2005 and December 2019 and 733 CT studies of controls with normal pancreas obtained between the same period in a tertiary referral center were retrospectively collected for developing an automatic end-to-end computer-aided detection (CAD) tool for PC using two-dimensional (2D) and three-dimensional (3D) radiomic analysis with machine learning. The CAD tool was tested in a nationwide dataset comprising 1,477 CT studies (671 PCs, 806 controls) obtained from institutions throughout Taiwan. RESULTS: The CAD tool achieved 0.918 (95% CI, 0.895-0.938) sensitivity and 0.822 (95% CI, 0.794-0.848) specificity in differentiating between studies with and without PC (area under curve 0.947, 95% CI, 0.936-0.958), with 0.707 (95% CI, 0.602-0.797) sensitivity for tumors < 2 cm. The positive and negative likelihood ratios of PC were 5.17 (95% CI, 4.45-6.01) and 0.10 (95% CI, 0.08-0.13), respectively. Where high specificity is needed, using 2D and 3D analyses in series yielded 0.952 (95% CI, 0.934-0.965) specificity with a sensitivity of 0.742 (95% CI, 0.707-0.775), whereas using 2D and 3D analyses in parallel to maximize sensitivity yielded 0.915 (95% CI, 0.891-0.935) sensitivity at a specificity of 0.791 (95% CI, 0.762-0.819). CONCLUSIONS: The high accuracy and robustness of the CAD tool supported its potential for enhancing the detection of PC.

Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study.

Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic cancer at CT. Materials and Methods Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test. Results A total of 546 patients with pancreatic cancer (mean age, 65 years ± 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference (P = .11) in sensitivity compared with the original radiologist report (96.1% [98 of 102]; 95% CI: 90.3, 98.9). In a test set of 1473 real-world CT studies (669 malignant, 804 control) from institutions throughout Taiwan, the DL tool distinguished between CT malignant and control studies with 89.7% (600 of 669; 95% CI: 87.1, 91.9) sensitivity and 92.8% specificity (746 of 804; 95% CI: 90.8, 94.5) (AUC, 0.95; 95% CI: 0.94, 0.96), with 74.7% (68 of 91; 95% CI: 64.5, 83.3) sensitivity for malignancies smaller than 2 cm. Conclusion The deep learning-based tool enabled accurate detection of pancreatic cancer on CT scans, with reasonable sensitivity for tumors smaller than 2 cm. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Aisen and Rodrigues in this issue.

Identification of fish species through tRNA-based primer design.

BACKGROUND: The correct establishment of the barcode classification system for fish can facilitate biotaxonomists to distinguish fish species, and it can help the government to verify the authenticity of the ingredients of fish products or identify unknown fish related samples. The Cytochrome c oxidation I (COI) gene sequence in the mitochondria of each species possesses unique characteristics, which has been widely used as barcodes in identifying species in recent years. Instead of using COI gene sequences for primer design, flanking tRNA segments of COI genes from 2618 complete fish mitochondrial genomes were analyzed to discover suitable primers for fish classification at taxonomic family level. The minimal number of primer sets is designed to effectively distinguish various clustered groups of fish species for identification applications. Sequence alignment analysis and cross tRNA segment comparisons were applied to check and ensure the primers for each cluster group are exclusive. RESULTS: Two approaches were applied to improve primer design and re-cluster fish species. The results have shown that exclusive primers for 2618 fish species were successfully discovered through in silico analysis. In addition, we applied sequence alignment analysis to confirm that each pair of primers can successfully identify all collected fish species at the taxonomic family levels. CONCLUSIONS: This study provided a practical strategy to discover unique primers for each fishery species and a comprehensive list of exclusive primers for extracting COI barcode sequences of all known fishery species. Various applications of verification of fish products or identification of unknown fish species could be effectively achieved.

Identification of key genes and signaling pathways related to Hetian sheep wool density by RNA-seq technology.

Hetian sheep is a breed of sheep unique to the Hetian area of Xinjiang whose wool is used for producing blankets. Individual differences and hair follicle density are the key factors affecting wool production. Therefore, this study aimed to assess the Hetian sheep having different wool densities to statistically analyze the wool traits and hair follicle parameters. Furthermore, the transcriptome sequencing analysis was performed on the skins with different wool densities. The results showed that wool quantity and total hair follicle density of the high wool density sheep was significantly higher than low wool density sheep. The sheepskin with high wool density was found to grow more and finer wool than sheepskin with low wool density. A total of 1,452 differentially expressed genes were screened from the two sets of samples, including 754 upregulated and 698 downregulated genes. The differentially expressed genes were involved in the TGF-ß/BMP and MAPK signaling pathways related to hair growth. Eleven differentially expressed genes belonging to the KAPs and KIFs might affect the fineness of the wool. The key genes, like the TNF, MAP2K2, INHBA, FST, PTPN11, MAP3K7, KIT, and BMPR1A, were found to probably affect the growth and density of the wool. The qPCR verified eight genes related to the MAPK pathway whose gene expression trends were consistent with the transcriptome sequencing results. This study furnishes valuable resources for enhancing the quality and production of wool in the Hetian sheep.

Bioactive Small Molecule Enhances Skin Burn Wound Healing and Hair Follicle Regeneration by Activating PI3K/AKT Signaling Pathway: A Preclinical Evaluation in Animal Model.

Rational: A bioactive small molecule of precision medicine involves targeted therapies. Shikonin, a herbal extract, is an active small molecule that is traditionally used in wound healing for its anti-tumor and anti-inflammatory properties. Therefore, the present study aims to evaluate the anti-inflammatory role of shikonin in skin burn wound healing and hair follicle regeneration and to identify molecular signaling pathways that promote the regeneration. Method: A secondary skin burn model of mice was established by conventional method. The burn wound was externally treated with shikonin ointment and excipient treated mice were used as controls. Skin samples were taken on the day 3 and 7 after drug treatment and the dosage was unified in the experiments. The wound healing process was observed by histopathological and immunofluorescence (IF) staining. The proliferation of hair follicle cells in wound skin was tracked by 5-Ethynyl-2'-deoxyuridne (EdU) staining. The inflammatory factors at the wound healing site were quantified by polymerase chain reaction (qPCR). The PI3K/Akt, P65, Ki67 signaling proteins and Bax/BCL2 apoptosis proteins were studied by western blot analysis. The functionality of PI3K/Akt signaling pathway was tested using LY294002, an inhibitor of PI3K. Result: Shikonin treated mice group exhibited better and faster skin burn wound healing in comparison with the controls. The proliferation of new skin cells and hair follicle regeneration in the wound site of the shikonin treated group was more active. The recruitment of macrophages in shikonin treated group was inhibited inturn decreased the expression of inflammatory factors. However, LY294002 inhibited the shikonin-mediated PI3K/Akt signaling pathway and affected the wound healing process. Conclusion: In conclusion, this study strengthens the hypothesis that bioactive small molecule, shikonin, inhibits inflammation, promotes wound healing and has a significant protective effect on the deep hair follicles against burn skin injury by activating the PI3K/Akt signaling pathway.

Achieving a combination of decent biocompatibility and large near-linear-elastic deformation behavior in shell-core-like structural TiNb/NiTi composite.

As expected from the material design, a novel shell-core-like structural TiNb/NiTi composite possessing both decent biocompatibility and large near-linear-elastic deformation behavior (namely as near-linear elasticity accompanied by high elastic strain limit) was prepared successfully by a hot pack-rolling combined with cold rolling procedure. Non-cytotoxic TiNb outer shell obstructs the NiTi inner core from cells and provides the decent biocompatibility of TiNb/NiTi composite. Large near-linear-elastic deformation behavior for this TiNb/NiTi composite has been confirmed to be associated with intrinsic elastic deformation, two types of reversible stress-induced martensitic transformations (i.e. ß↔α'' and B2↔B19' transformations) occurring in a homogeneous manner, together with the (001) compound twin in B19' martensitic plates. Our study provides a new design approach for developing NiTi-based composites with both decent biocompatibility and large near-linear-elastic deformation behavior for biomedical or engineering applications.

Applications of artificial intelligence in pancreatic and biliary diseases.

The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.

Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.

BACKGROUND: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks' potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation. METHODS: In this retrospective, diagnostic study, contrast-enhanced CT images of 370 patients with pancreatic cancer and 320 controls from a Taiwanese centre were manually labelled and randomly divided for training and validation (295 patients with pancreatic cancer and 256 controls) and testing (75 patients with pancreatic cancer and 64 controls; local test set 1). Images were preprocessed into patches, and a CNN was trained to classify patches as cancerous or non-cancerous. Individuals were classified as with or without pancreatic cancer on the basis of the proportion of patches diagnosed as cancerous by the CNN, using a cutoff determined using the training and validation set. The CNN was further tested with another local test set (101 patients with pancreatic cancers and 88 controls; local test set 2) and a US dataset (281 pancreatic cancers and 82 controls). Radiologist reports of pancreatic cancer images in the local test sets were retrieved for comparison. FINDINGS: Between Jan 1, 2006, and Dec 31, 2018, we obtained CT images. In local test set 1, CNN-based analysis had a sensitivity of 0·973, specificity of 1·000, and accuracy of 0·986 (area under the curve [AUC] 0·997 (95% CI 0·992-1·000). In local test set 2, CNN-based analysis had a sensitivity of 0·990, specificity of 0·989, and accuracy of 0·989 (AUC 0·999 [0·998-1·000]). In the US test set, CNN-based analysis had a sensitivity of 0·790, specificity of 0·976, and accuracy of 0·832 (AUC 0·920 [0·891-0·948)]. CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011-0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1-1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0-3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set. INTERPRETATION: CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation. FUNDING: Taiwan Ministry of Science and Technology.

Efficacy of more intensive lipid-lowering therapy on cardiovascular diseases: a systematic review and meta-analysis.

BACKGROUND: Cardiovascular disease is the leading cause of morbidity and mortality with incidence rates of 5-10 per 1000 person-years, according to primary prevention studies. To control hyperlipidemia-a major risk factor of cardiovascular disease-initiation of lipid-lowering therapy with therapeutic lifestyle modification or lipid-lowering agent is recommended. Few systematic reviews and meta-analyses are available on lipid-lowering therapy for the primary prevention of cardiovascular diseases. In addition, the operational definitions of intensive lipid-lowering therapies are heterogeneous. The aim of our study was to investigate whether intensive lipid-lowering therapies reduce greater cardiovascular disease risks in primary prevention settings. METHODS: MEDLINE, EMBASE, and Cochrane Library databases were searched from inception to March 2019 for randomized controlled trials. We used random effects model for overall pooled risk ratio (RR) estimation with cardiovascular events of interest and all-cause mortality rate for the intensive lipid-lowering group using the standard lipid-lowering group as the reference. The Cochrane Risk of Bias Tool was used for quality assessment. RESULTS: A total of 18 randomized controlled trials were included. The risk reductions in cardiovascular outcomes and all-cause mortality associated with more intensive vs. standard lipid-lowering therapy across all trials were 24 and 10%, respectively (RR 0.76, 95% confidence interval 0.68-0.85; RR 0.90, 95% confidence interval 0.83-0.97); however, the risk reduction varied by baseline LDL-C level in the trial. A greater risk reduction was noted with higher LDL-C level. Intensive lipid-lowering for coronary heart disease protection was more pronounced in the non-diabetic populations than in the diabetic populations. CONCLUSIONS: More intensive LDL-C lowering was associated with a greater reduction in risk of total and cardiovascular mortality in trials of patients with higher baseline LDL-C levels than less intensive LDL-C lowering. Intensive lipid-lowering was associated with a significant risk reduction of coronary heart disease and must be considered even in the non-diabetic populations.

Analysis of the Prognosis and Therapeutic Value of the CXC Chemokine Family in Head and Neck Squamous Cell Carcinoma.

The CXC chemokines belong to a family which includes 17 different CXC members. Accumulating evidence suggests that CXC chemokines regulate tumor cell proliferation, invasion, and metastasis in various types of cancers by influencing the tumor microenvironment. The different expression profiles and specific function of each CXC chemokine in head and neck squamous cell carcinoma (HNSCC) are not yet clarified. In our work, we analyzed the altered expression, interaction network, and clinical data of CXC chemokines in patients with HNSCC by using the following: the Oncomine dataset, cBioPortal, Metascape, String analysis, GEPIA, and the Kaplan-Meier plotter. The transcriptional level analysis suggested that the mRNA levels of CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL8, CXCL9, CXCL10, CXCL11, and CXCL13 increased in HNSCC tissue samples when compared to the control tissue samples. The expression levels of CXCL9, CXCL10, CXCL11, CXCL12, and CXCL14 were associated with various tumor stages in HNSCC. Clinical data analysis showed that high transcription levels of CXCL2, CXCL3, and CXCL12, were linked with low relapse-free survival (RFS) in HNSCC patients. On the other hand, high CXCL14 levels predicted high RFS outcomes in HNSCC patients. Meanwhile, increased gene transcription levels of CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 were associated with a higher overall survival (OS) advantage in HNSCC patients, while high levels of CXCL1, and CXCL8 were associated with poor OS in all HNSCC patients. This study implied that CXCL1, CXCL2, CXCL3, CXCL8, and CXCL12 could be used as prognosis markers to identify low survival rate subgroups of patients with HNSCC as well as be potential suitable therapeutic targets for HNSCC patients. Additionally, CXCL9, CXCL10, CXCL13, CXCL14, and CXCL17 could be used as functional prognosis biomarkers to identify better survival rate subgroups of patients with HNSCC.

Opportunities of circulating tumor DNA in lung cancer.

Current classification and treatment of lung cancer rely increasingly on molecular and genetic testing. Obtaining tumor tissue is not always feasible and multiple biopsies are undesirable. In response to the demand for non-invasive molecular and genetic testing in cancer care, several liquid biopsy technologies, including circulating DNA (ctDNA), have been developed. ctDNA analysis is now technically feasible to be carried out in large scales and integrated into clinical practice owing to the advances in technology. Despite the challenges in improving test accuracy and cost-effectiveness, there are huge potentials for ctDNA analysis in lung cancer management. This review focuses on the clinical utility of ctDNA analysis in lung cancer, including early detection, monitoring treatment response and detecting residual disease, identification of genetic determinants for targeted therapy, and predicting efficacy of immune checkpoint blockade.

Weaning outcome of solid cancer patients requiring mechanical ventilation in the intensive care unit.

BACKGROUND: Whether the weaning outcome of solid cancer patients receiving mechanical ventilation (MV) in the intensive care unit (ICU) is comparable to that in non-cancer patients is unknown. The aim of this study was to compare the weaning outcomes between non-cancer patients and patients with different types of cancer. METHODS: We studied patients requiring MV during ICU stay for medical reasons between 2012 and 2014. Cancer patients were grouped into those with lung cancer (LC), head and neck cancer (HNC), hepatocellular carcinoma (HCC), and other cancers (OC). The primary endpoint was successful weaning at day 90 after the initiation of MV, and the main secondary endpoints were 28-day and 90-day mortality after ICU admission. RESULTS: Five hundred and eighteen patients with solid cancers and 1362 non-cancer patients were recruited. The rate of successful weaning at day 90 was 57.9% in cancer patients, which was lower than 68.9% in non-cancer patients (p < 0.001). Compared to non-cancer patients, LC was associated with a lower probability of weaning at day 90 (hazard ratio 0.565, 95% CI 0.446 to 0.715), while HNC, HCC, and OC had similar probabilities. The 28-day and 90-day mortality rates were higher in cancer patients than in non-cancer patients (45.2% vs. 29.4%, and 65.6% vs. 37.7%, respectively, both p < 0.001). CONCLUSION: Among mechanically ventilated patients in the ICU, those with LC were associated with a lower probability of weaning at day 90 compared to non-cancer patients.

Prognostic factors and treatment outcomes of malignant pleural mesothelioma in Eastern Asian patients - A Taiwanese study.

BACKGROUND/PURPOSE: There are scarce reports on the prognostic factors and treatment outcomes of patients with malignant pleural mesothelioma (MPM) in Asia. This study aimed to address these matters in a real-world setting. METHODS: Medical records of patients with histologically proven MPM diagnosed between 1977 and 2016 at the National Taiwan University Hospital were reviewed. Variables including age, gender, performance status, asbestos exposure, smoking history, histology subtype, staging, and treatment received were recorded. All patients were followed until death or March 1st, 2017. Survival and prognostic factors were analyzed by the Kaplan-Meir method and the Cox proportional hazard model. RESULTS: A total of 93 patients was identified, including 65 men and 28 women. An increasing trend of MPM cases diagnosed was observed in the past 40 years. Stage I/II disease (HR 0.24, 95% CI 0.13-0.46) and epithelioid histology (HR 0.42, 95% CI 0.23-0.75) were associated with favorable prognosis, whereas age ≥70 years (HR 2.66, 95% CI 1.36-5.22) and ECOG ≥2 (HR 5.03, 95% CI 2.69-9.4) were poor prognostic factors. After adjustment for prognostic factors, surgery in stage I-III MPM (HR 0.36, 95% CI 0.15-0.83) and systemic therapy in stage III/IV disease (HR 0.42, 95% CI 0.19-0.94) conferred a survival benefit. CONCLUSION: This is one of the largest case series of MPM reported in Asia outside of Japan. Prognostic factors in the study population included age, performance status, stage, and histology subtype. Surgery in potentially resectable disease and systemic therapy in advanced MPM confer a survival benefit in Asian patients.

Best Response According to RECIST During First-line EGFR-TKI Treatment Predicts Survival in EGFR Mutation-positive Non-Small-cell Lung Cancer Patients.

INTRODUCTION: The association between the response to first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) and survival in EGFR mutation-positive non-small-cell lung cancer (NSCLC) remains unclear. We studied the association between the response to first-line EGFR-TKIs and survival using Response Evaluation Criteria In Solid Tumors (RECIST) and maximal tumor shrinkage. MATERIALS AND METHODS: We analyzed data from patients with advanced EGFR mutation-positive NSCLC enrolled in first-line gefitinib and afatinib trials. A total of 98 patients who achieved a response or stable disease and had ≥ 1 measurable target lesion were included. The association between the best response by RECIST or maximal tumor shrinkage and survival was analyzed in Kaplan-Meier and Cox regression models with the landmark method. The specified landmark time points were 8 weeks, the median time to maximal tumor shrinkage (16.5 weeks), and median progression-free survival (PFS; 56 weeks). RESULTS: A total of 76 patients (77%) responded to gefitinib or afatinib. Of these 76 patients, 49 (64%) and 75 (99%) had achieved a response at 8 and 16.5 weeks, respectively. All responders had achieved a response by 56 weeks. The responders had a significantly longer PFS and overall survival (OS) compared with those with stable disease at 16.5 weeks (PFS, P = .003; OS, P < .001) and 56 weeks (PFS, P = .026; OS, P = .016) but not at 8 weeks (PFS, P = .104; OS, P = .313). Among the responders, greater tumor shrinkage was not associated with longer PFS or OS. CONCLUSION: Those with a response to first-line gefitinib or afatinib had more favorable PFS and OS compared with those with stable disease. A sufficient observation period was required for the response to occur and predict outcomes. Greater maximal tumor shrinkage in the responders was not predictive of survival.