Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. METHODS: This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data.

Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination (R2 = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.

Insight into the Types of Alkanolamines on the Properties of Copper(II) Formate-Based Conductive Ink.

Conductive inks are one of the most important functional materials for printed flexible electronic devices, and their properties determine the methods of subsequent patterning and metallization. In comparison with copper nanoparticle or nanowire inks, copper particle-free inks employing copper(II) formate (Cuf) as a precursor have attracted the interest of researchers due to their flexibility in preparation, excellent stability, and lower conversion temperature. Alkanolamines can provide Cuf with excellent solubility in alcohols while being less toxic and having a certain reducibility, making them preferable ligands in comparison with aliphatic amines and pyridine. However, there have been few studies on the effects of the alkanolamine types on the performance of Cuf inks. Also, the decomposition mechanism of copper-alkanolamine complex inks is not clear. In this work, different kinds of alkanolamines were chosen as ligands to formulate Cuf inks to address the mentioned issues. The influences of amine types on the stability, wettability, thermal decomposition behavior, and electrical performance of the formulated Cuf particle-free inks were investigated in detail. The results show that the utilization of alkanolamines could provide Cuf with excellent solubility in alcohols, resulting in an ink with good stability and favorable wetting properties. The thermal decomposition temperature and electrical performance of the formulated copper ink are largely dependent on the amine used. When amines with a longer carbon chain and more branches were utilized to prepare the ink, a decreased decomposition temperature was observed on the derived inks because of the steric hindrance effect. Copper films with good morphology and conductivity could be obtained at low temperatures by selecting the appropriate alkanolamine. Copper particle-free conductive ink from 2-amino-2-methyl-1-propanol demonstrated better morphology and electrical performance (16.09 µΩ·cm) and was successfully used for conductive circuits by direct-writing.

Streptomyces sungeiensis SD3 as a Microbial Chassis for the Heterologous Production of Secondary Metabolites.

We present the newly isolated Streptomyces sungeiensis SD3 strain as a promising microbial chassis for heterologous production of secondary metabolites. S. sungeiensis SD3 exhibits several advantageous traits as a microbial chassis, including genetic tractability, rapid growth, susceptibility to antibiotics, and metabolic capability supporting secondary metabolism. Genomic and transcriptomic sequencing unveiled the primary metabolic capabilities and secondary biosynthetic pathways of S. sungeiensis SD3, including a previously unknown pathway responsible for the biosynthesis of streptazone B1. The unique placement of S. sungeiensis SD3 in the phylogenetic tree designates it as a type strain, setting it apart from other frequently employed Streptomyces chassis. This distinction makes it the preferred chassis for expressing biosynthetic gene clusters (BGCs) derived from strains within the same phylogenetic or neighboring phylogenetic clade. The successful expression of secondary biosynthetic pathways from a closely related yet slow-growing strain underscores the utility of S. sungeiensis SD3 as a heterologous expression chassis. Validation of CRISPR/Cas9-assisted genetic tools for chromosomal deletion and insertion paved the way for further strain improvement and BGC refactoring through rational genome editing. The addition of S. sungeiensis SD3 to the heterologous chassis toolkit will facilitate the discovery and production of secondary metabolites.

A PilZ domain protein interacts with the transcriptional regulator HinK to regulate type VI secretion system in Pseudomonas aeruginosa.

Type VI secretion systems (T6SS) are bacterial macromolecular complexes that secrete effectors into target cells or the extracellular environment, leading to the demise of adjacent cells and providing a survival advantage. Although studies have shown that the T6SS in Pseudomonas aeruginosa is regulated by the Quorum Sensing system and second messenger c-di-GMP, the underlying molecular mechanism remains largely unknown. In this study, we discovered that the c-di-GMP-binding adaptor protein PA0012 has a repressive effect on the expression of the T6SS HSI-I genes in P. aeruginosa PAO1. To probe the mechanism by which PA0012 (renamed TssZ, Type Six Secretion System -associated PilZ protein) regulates the expression of HSI-I genes, we conducted yeast two-hybrid screening and identified HinK, a LasR-type transcriptional regulator, as the binding partner of TssZ. The protein-protein interaction between HinK and TssZ was confirmed through co-immunoprecipitation assays. Further analysis suggested that the HinK-TssZ interaction was weakened at high c-di-GMP concentrations, contrary to the current paradigm wherein c-di-GMP enhances the interaction between PilZ proteins and their partners. Electrophoretic mobility shift assays revealed that the non-c-di-GMP-binding mutant TssZR5A/R9A interacts directly with HinK and prevents it from binding to the promoter of the quorum-sensing regulator pqsR. The functional connection between TssZ and HinK is further supported by observations that TssZ and HinK impact the swarming motility, pyocyanin production, and T6SS-mediated bacterial killing activity of P. aeruginosa in a PqsR-dependent manner. Together, these results unveil a novel regulatory mechanism wherein TssZ functions as an inhibitor that interacts with HinK to control gene expression.

Initial cyclic-di-GMP upregulation triggers sporadic cellular expansion leading to improved cellular survival.

Bacterial second messenger c-di-GMP upregulation is associated with the transition from planktonic to sessile microbial lifestyle, inhibiting cellular motility, and virulence. However, in-depth elucidation of the cellular processes resulting from c-di-GMP upregulation has not been fully explored. Here, we report the role of upregulated cellular c-di-GMP in promoting planktonic cell growth of Escherichia coli K12 and Pseudomonas aeruginosa PAO1. We found a rapid expansion of cellular growth during initial cellular c-di-GMP upregulation, resulting in a larger planktonic bacterial population. The initial increase in c-di-GMP levels promotes bacterial swarming motility during the growth phase, which is subsequently inhibited by the continuous increase of c-di-GMP, and ultimately facilitates the formation of biofilms. We demonstrated that c-di-GMP upregulation triggers key bacterial genes linked to bacterial growth, swarming motility, and biofilm formation. These genes are mainly controlled by the master regulatory genes csgD and csrA. This study provides us a glimpse of the bacterial behavior of evading potential threats through adapting lifestyle changes via c-di-GMP regulation.

A multimodal magnetoelastic artificial skin for underwater haptic sensing.

Future exploitation of marine resources in a sustainable and eco-friendly way requires autonomous underwater robotics with human-like perception. However, the development of such intelligent robots is now impeded by the lack of adequate underwater haptic sensing technology. Inspired by the populational coding strategy of the human tactile system, we harness the giant magnetoelasticity in soft polymer systems as an innovative platform technology to construct a multimodal underwater robotic skin for marine object recognition with intrinsic waterproofness and a simple configuration. The bioinspired magnetoelastic artificial skin enables multiplexed tactile modality in each single taxel and obtains an impressive classification rate of 95% in identifying seven types of marine creatures and marine litter. By introducing another degree of freedom in underwater haptic sensing, this work represents a milestone toward sustainable marine resource exploitation.

Factors predicting indistinct plane of surgical capsule in patients underwent HoLEP procedures.

PURPOSE: To evaluate factors that effectively predict indistinct plane (IP) in patients who underwent holmium laser enucleation of the prostate (HoLEP). METHODS: Data of 208 consecutive patients from our HoLEP database were reviewed and analyzed. IP was defined in 107 cases, as the plane could be identified only depending on endoscopic beak dissection rather than laser dissection in the initial stage of HoLEP, whereas the control group consisted of 101 cases. Variables including age, body mass index, prostatic volume (PV), intravesical prostatic protrusion, prostate-specific antigen, prostate-specific antigen density, bladder stones, urinary tract infection, microscopic hematuria, prior biopsy (PB), diabetes, hypertension, history of acute urinary retention, 5-alpha reductase inhibitor treatment, catheter dependency, residual urine, region, smoking, and alcohol consumption were compared between the two groups. The risk factors for predicting the presence of IP were determined using a multivariable binary logistic regression model using a forward selection approach with a focus on improvement in the area under the receiver operating characteristic curve (AUC). RESULTS: The incidence of IP was 51.4% (107/208). PV (OR = 0.977, p < 0.001) and PB (OR = 0.297, p = 0.028) were identified as the independent predictors of capsule plane status. PV with a cutoff of 54 ml had the best predictive effectiveness for IP based on AUC (0.727; 95% CI 0.659-0.795). The specificity and sensitivity of this cutoff were 82.2% and 53.3%, respectively. CONCLUSION: PV is the most reliable factor to predict IP during HoLEP procedures. There is a high possibility of IP in patients with a PV less than 54 ml.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

Biosynthesis of Octacosamicin A: Uncommon Starter/extender Units and Product Releasing via Intermolecular Amidation.

Octacosamicin A is an antifungal metabolite featuring a linear polyene-polyol chain flanked by N-hydroxyguanidine and glycine moieties. We report here that sub-inhibitory concentrations of streptomycin elicited the production of octacosamicin A in Amycolatopsis azurea DSM 43854T . We identified the biosynthetic gene cluster (oca BGC) that encodes a modular polyketide synthase (PKS) system for assembling the polyene-polyol chain of octacosamicin A. Our analysis suggested that the N-hydroxyguanidine unit originates from a 4-guanidinobutyryl-CoA starter unit, while the PKS incorporates an α-hydroxyketone moiety using a (2R)-hydroxymalonyl-CoA extender unit. The modular PKS system contains a non-canonical terminal module that lacks thioesterase (TE) and acyl carrier protein (ACP) domains, indicating the biosynthesis is likely to employ an unconventional and cryptic off-loading mechanism that attaches glycine to the polyene-polyol chain via an intermolecular amidation reaction.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

BACKGROUND: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. PURPOSE: This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. METHODS: A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). RESULTS: The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis. CONCLUSIONS: The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.

OBJECTIVE: The objective of this study was to explore the application of radiomics combined with machine learning to establish different models to assist in the diagnosis of venous wall invasion in patients with renal cell carcinoma and venous tumor thrombus and to evaluate the diagnostic efficacy. MATERIALS AND METHODS: We retrospectively reviewed the data of 169 patients in Peking University Third Hospital from March 2015 to January 21, who was diagnosed as renal mass with venous invasion. According to the intraoperative findings, 111 patients were classified to the venous wall invasion group and 58 cases in the non-invasion group. ITK-snap was used for tumor segmentation and PyRadiomics 3.0.1 package was used for feature extraction. A total of 1598 features could be extracted from each CT image. The patients were divided into training set and testing set by time. The elastic-net regression with 4-fold cross-validation was used as a dimension-reduction method. After feature selection, a support vector machines (SVM) model, a logistic regression (LR) model, and an extra trees (ET) model were established. Then the sensitivity, specificity, accuracy, and the area under the curve (AUC) were calculated to evaluate the diagnostic performance of each model on the testing set. RESULTS: Patients before September 2019 were divided into the training set, of which 88 patients were in the invasion group and 42 patients were in the non-invasion group. The others were in the testing set, of which 32 patients were in the invasion group and 16 patients were in the non-invasion group. A total of 34 radiomics features were obtained by the elastic-net regression. The SVM model had an AUC value of 0.641 (95% CI, 0.463-0.769), a sensitivity of 1.000, and a specificity of 0.062. The LR model had an AUC value of 0.769 (95% CI, 0.620-0.877), a sensitivity of 0.913, and a specificity of 0.312. The ET model had an AUC value of 0.853 (95% CI, 0.734-0.948), a sensitivity of 0.783, and a specificity of 0.812. Among the 3 models, the ET model had the best diagnostic effect, with a good balance of sensitivity and specificity. And the higher the tumor thrombus grade, the better the diagnostic efficacy of the ET model. In inferior vena cava tumor thrombus, the sensitivity, specificity, accuracy, and AUC of ET model can be improved to 0.889, 0.800, 0.857, 0.878 (95% CI, 0.745-1.000). CONCLUSION: Machine learning combined with radiomics method can effectively identify whether venous wall was invaded by tumor thrombus and has high diagnostic efficacy with an AUC of 0.853 (95% CI, 0.734-0.948).

Physiologic changes in the hepatopulmonary syndrome before and after liver transplant: A longitudinal and predictor analysis.

BACKGROUND AND AIMS: Hepatopulmonary syndrome (HPS) is a common complication of liver disease defined by abnormal oxygenation and intrapulmonary vascular dilatation, treated with liver transplantation. Little is known about changes in HPS physiological parameters over time. We sought to describe baseline clinical and physiological characteristics in HPS and their relationships, temporal changes in physiological parameters before and after transplant, and predictors of changes in oxygenation. APPROACH AND RESULTS: This was a retrospective cohort study in the Canadian HPS Program (n = 132). Rates of change after diagnosis were: -3.7 (-6.4, -0.96) mm Hg/year for partial pressure of arterial oxygen (PaO 2 ); -26 (-96, 44) m/year for 6-minute walk distance, and 3.3% (-6.6, -0.011) predicted/year for diffusion capacity. Noninvasive shunt of ≥ 20% predicted a slower PaO 2 decline by 0.88 (0.36, 1.4) mm Hg/month. We identified 2 PaO 2 deterioration classes-"very severe disease, slow decliners" (PaO 2 45.0 mm Hg; -1.0 mm Hg/year); and "moderate disease, steady decliners" (PaO 2 65.5 mm Hg; -2.5 mm Hg/year). PaO 2 increased by 6.5 (5.3, 7.7) mm Hg/month in the first year after transplant. The median time to normalization was 149 (116, 184) days. Posttransplant improvement in PaO 2 was 2.5 (0.1, 4.9) mm Hg/month faster for every 10 mm Hg greater pretransplant orthodeoxia. CONCLUSIONS: We present a large and long longitudinal data analysis in HPS. In addition to rates of physiological decline and improvement before and after liver transplantation, we present novel predictors of PaO 2 decline and improvement rates. Our findings enhance our understanding of the natural history of HPS and provide pathophysiologic clues. Importantly, they may assist providers in prognostication and prioritization before and after transplant.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

Bony Congenital Nasolacrimal Duct Obstruction: A Novel Phenotype of Aplasia of Lacrimal and Major Salivary Glands.

PURPOSE: Aplasia of lacrimal and salivary glands (ALSG) is a syndromic disorder characterized by aplasia of lacrimal and salivary systems. Reported ophthalmic manifestations of ALSG include aplasia of lacrimal glands, punctal agenesis, lacrimal sac mucocele, and membranous congenital nasolacrimal duct obstruction (CNLDO). Bony CNLDO, a rare clinical entity, has not been associated with any syndromic disorder. This study investigated the relationship between genetic mutations and bony CNLDO in 3 Chinese families with ALSG. DESIGN: Single-center observational case study. PARTICIPANTS: Three Chinese families with bony CNLDO, including 7 affected and 9 healthy family members. METHODS: Slit-lamp ophthalmic examination, comprehensive physical examination, orbital computed tomography (CT) imaging, cervicofacial magnetic resonance imaging, audiometry, and whole exome sequencing on periphery blood were performed. Variants were cross-referenced with 1000 control genomes and various population databases. Pathologic variants were identified using bioinformatic tools. MAIN OUTCOME MEASURES: Clinical examination, diagnostic imaging, whole exome sequencing, and bioinformatic analysis findings. RESULTS: Affected patients showed decreased tear production on the Schimer I test and reduced tear breakup time. Bony CNLDO was observed on CT, showing unilateral or bilateral bony termination at the middle or terminal segment of the nasolacrimal canal. Magnetic resonance imaging showed aplasia or absence of lacrimal, parotid, and submandibular glands. Physical examination revealed normal ears, digits, and facial morphology. Audiometry and dental assessment were conducted on the pediatric patients and yielded normal results. The clinical characteristics of patients aligned with a diagnosis of ALSG. Genomic analysis revealed 3 novel heterozygous missense mutations of the Fgf10 gene: c.316T→C, c.327C→G, and c.332T→G. The inheritance pattern was autosomal dominant with variable penetrance. These variants were not observed in 1000 control genomes and population databases. These variant positions also were shown to be highly conserved across various animal species. Mutated genes and proteins were predicted as deleterious with most computational models, with a few suggesting they may be benign. CONCLUSIONS: Bony CNLDO was identified as a novel phenotype of ALSG implicated by missense mutations of highly conserved residues in the Fgf10 gene. These cases broadened our knowledge of Fgf10-related phenotypes and prompted clinicians to consider syndromic associations in patients with bony CNLDO. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Prevention, incidence, and risk factors of chyle leak after radical nephrectomy and thrombectomy.

BACKGROUND: To define the incidence and risk factors of chyle leak (CL) after radical nephrectomy and thrombectomy and to determine the impact of chyle leak on oncological outcomes. PATIENTS AND METHODS: A total of 445 patients who underwent radical nephrectomy and thrombectomy between January 2014 and January 2023 were included. CL is defined as the drainage of chyle with a triglyceride level greater than 110 mg/dL after oral intake or enteral nutrition. Multivariate logistic regression analysis was performed to identify the risk factors of postoperative (CL). The Kaplan-Meier curves were used to compare overall survival and cancer-specific survival. RESULTS: 44 patients (9.9%) were diagnosed as (CL). All patients developed CL within 6 days after the operation with a median time of 3 days. In multivariate logistic regression analysis, Mayo grade and side were independent patient-related risk factors. In addition, operation approach, operation time, and number of lymph nodes harvested were independent surgery-related risk factors. Between the CL group and the non-CL group, neither overall survival nor cancer-specific survival showed statistical differences. CONCLUSION: Based on this retrospective study of renal cell carcinoma and tumor thrombus patients in our center, we found that the risk factors were Mayo grade, side, operation approach, operation time, and number of lymph nodes harvested, and the occurrence of CL significantly prolonged hospital stay, but had no effect on long-term oncological outcomes.

Progress on Two-Dimensional Transitional Metal Dichalcogenides Alloy Materials: Growth, Characterisation, and Optoelectronic Applications.

Two-dimensional (2D) transitional metal dichalcogenides (TMDs) have garnered remarkable attention in electronics, optoelectronics, and hydrogen precipitation catalysis due to their exceptional physicochemical properties. Their utilisation in optoelectronic devices is especially notable for overcoming graphene's zero-band gap limitation. Moreover, TMDs offer advantages such as direct band gap transitions, high carrier mobility, and efficient switching ratios. Achieving precise adjustments to the electronic properties and band gap of 2D semiconductor materials is crucial for enhancing their capabilities. Researchers have explored the creation of 2D alloy phases through heteroatom doping, a strategy employed to fine-tune the band structure of these materials. Current research on 2D alloy materials encompasses diverse aspects like synthesis methods, catalytic reactions, energy band modulation, high-voltage phase transitions, and potential applications in electronics and optoelectronics. This paper comprehensively analyses 2D TMD alloy materials, covering their growth, preparation, optoelectronic properties, and various applications including hydrogen evolution reaction catalysis, field-effect transistors, lithium-sulphur battery catalysts, and lasers. The growth process and characterisation techniques are introduced, followed by a summary of the optoelectronic properties of these materials.

Gas Evolution Principle during Gas Drainage from a Drill Hole along the Coal Seam and Reasonable Borehole Layout Spacing.

The efficiency of coal seam gas drainage can be further improved by the accurate mastery of the gas evolution principle during gas drainage from the drill hole along the coal seam and reasonable optimization of borehole layout spacing. Based on the actual geological conditions of the no. 2 coal seam in a coal mine in Guizhou, China, and relevant control equations, a fluid-structure interaction model of gas drainage from a drill hole along the coal seam was established in this paper. Besides, numerical simulation research on the gas evolution principle during gas drainage along the coal seam and optimization of the borehole layout spacing was carried out with the COMSOL simulation software; and these were verified in combination with the project site. The results showed that in the early stage of gas drainage the gas pressure in the area near the drill holes decreased significantly. As the gas drainage went on, the degree of influence decreased gradually. During the gas drainage from adjacent drill holes, the gas pressure in the coal body between the holes decreased rapidly, and the migration was obvious. With the increase of the spacing between the drill holes, the drainage superposition effect between these holes gradually decreased until the influence area around a single hole is independently distributed in a circle-like shape, indicating that the optimization and the reasonable spacing of the drill holes are directly related to the effect of the gas drainage. With the increase of drilling spacing, the superposition effect of extraction between the holes gradually decreased until the influence area around a single hole is independently distributed in the shape of a circle, indicating that the optimization of drilling spacing is directly related to the effect of gas drainage. The results of numerical simulation were verified by analyzing the three-dimensional map of the gas pressure during the period of gas drainage at the project site and by comparing and examining the rational borehole layout spacing of the drill hole along the coal seam. The results of this study can be used to determine the spacing of gas extraction boreholes and improve the efficiency of gas extraction in the no. 2 coal seam of a coal mine in Guizhou, China, as well as to provide a reference for the gas pressure evolution, velocity field distribution, the prediction of effective drainage area, and the selection of rational borehole layout spacing during gas drainage.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Risk of ocular adverse events with aromatase inhibitors.

PURPOSE: Aromatase inhibitors (AIs) are a class of medications used for adjuvant treatment of breast cancer. Recent case reports suggest that AIs may be associated with various ocular adverse events (AEs). This study evaluates the risk of ocular AEs in patients who take AIs. METHOD: Disproportionality analysis was performed using data from the U.S. Food and Drug Administration's Adverse Events Reporting System database from 2004 to 2022. All cases of vitreomacular traction, macular edema, retinal deposits, retinal artery occlusion, macular hole, retinal hemorrhage, uveitis, retinal tear, retinal detachment, dry eye disease, blepharitis, and optic neuropathy were searched for the 3 AIs anastrozole, letrozole, and exemestane. A search also was performed on trastuzumab as a control. Reported odds ratios (RORs) and corresponding 95% CIs were computed. RESULTS: We identified 322 ocular AEs of interest for the 3 AIs and 55 for trastuzumab. Anastrozole had the most AEs (n = 163) and was found to have strong associations with vitreomacular traction (ROR = 665; 95% CI, 352-1255), macular edema (ROR = 37; 95% CI, 25-54), retinal deposits (ROR = 11; 95% CI, 2-77), and uveitis (ROR = 6; 95% CI, 4-9). Letrozole had strong associations with retinal deposits (ROR = 8, 95% CI, 1-57) and retinal artery occlusion (ROR = 6; 95% CI, 3-11). Exemestane had a strong association with macular holes (ROR = 10; 95% CI, 3-30). CONCLUSION: Disproportionality analysis revealed an increased risk of ocular AEs with each of the AIs. This study calls for clinicians, especially oncologists and ophthalmologists, to be vigilant in patients who are on AI therapy, allowing them to provide prompt interventions to mitigate further ocular morbidities.