The oviposition preference and offspring performance of Aethina tumida (Coleoptera: Nitidulidae).

Given the rapid spread and potential harm caused by the small hive beetle, Aethina tumida (Coleoptera: Nitidulidae) in China, it has become imperative to comprehend the developmental biology of this invasive species. Currently, there is limited knowledge regarding the impact of A. tumida female oviposition site preference on larval growth and development. To examine this, we investigated the ovipositional preference of adult female A. tumida on bee pupae, beebread, banana, and honey through a free choice test. Furthermore, we assessed the impact of these food resources on offspring performance, which included larval development time, survival, wandering larvae weight, emerged adult body mass, reproduction, and juvenile hormone titer. Our results showed that A. tumida females exhibited a strong preference for ovipositing on bee pupae compared to other diets, while showing reluctance toward honey. Moreover, A. tumida larvae that were fed on bee pupae displayed accelerated growth compared to those fed on other diets. Furthermore, A. tumida fed on bee pupae exhibited higher weights for wandering larvae, and emerged adult, increased pupation rates, enhanced fecundity and fertility, as well as a larger number of unilateral ovarioles during the larval stage when compared to those fed on other diets. Overall, the results indicate that the oviposition preferences of A. tumida females are adaptive, as their choices can enhance the fitness of their offspring. This finding aligns broadly with the hypothesis of oviposition preference and larval performance. This study can provide a foundation for the development of attractants aimed at promoting the oviposition of the A. tumida adults.

CO2 utilization for methanol production: a review on the safety concerns and countermeasures.

The catalytic conversion of carbon dioxide is one of the important ways to achieve the goal of carbon neutralization, which can be further divided into electrocatalysis, thermal catalysis, and photocatalysis. Although photocatalysis and electrocatalysis have the advantages of mild reaction conditions and low energy consumption, the thermal catalytic conversion of CO2 has larger processing capacity, better reduction effect, and more complete industrial foundation, which is a promising technology in the future. During the development of new technology from laboratory to industrial application, ensuring the safety of production process is essential. In this work, safety optimization design of equipment, safety performance of catalysts, accident types, and their countermeasures in the industrial applications of CO2 to methanol are reviewed and discussed in depth. Based on that, future research demands for industrial process safety of CO2 to methanol were proposed, which provide guidance for the large-scale application of CO2 thermal catalytic conversion technology.

Hierarchical Porous Bimetallic FeMn Metal-Organic Framework Gel for Efficient Activation of Peracetic Acid in Antibiotic Degradation.

Effective techniques for eliminating antibiotics from water environments are in high demand. The peracetic acid (PAA)-based advanced oxidation process has recently drawn increasing attention for its effective antibiotic degrading capability. However, current applications of PAA-based techniques are limited and tend to have unsatisfactory performance. An additional catalyst for PAA activation could provide a promising solution to improve the performance of PAA. Bulky metal-organic framework gels (MOGs) stand out as ideal catalysts for PAA activation owing to their multiple advantages, including large surface areas, high porosity, and hierarchical pore systems. Herein, a bimetallic hierarchical porous structure, i.e., FeMn13BTC, was synthesized through a facile one-pot synthesis method and employed for PAA activation in ofloxacin (OFX) degradation. The optimized FeMn MOG/PAA system exhibited efficient catalytic performance, characterized by 81.85% OFX degradation achieved within 1 h owing to the specific hierarchical structure and synergistic effect between Fe and Mn ions, which greatly exceeded the performance of the only PAA-catalyzed system. Furthermore, the FeMn MOG/PAA system maintained >80% OFX degradation in natural water. Quenching experiments, electron spin resonance spectra, and model molecular degradation revealed that the primary reactive oxygen species responsible for the catalytic effect was R-O•, especially CH3C(=O)OO•, with minor contributions of •OH and 1O2. Overall, introduction of the MOG catalyst strategy for PAA activation achieved high antibiotic degradation performance, establishing a paradigm for the design of heterogeneous hierarchical systems to broaden the scope of catalyzed water treatment applications.

Flexible, Transparent and Conductive Metal Mesh Films with Ultra-High FoM for Stretchable Heating and Electromagnetic Interference Shielding.

Despite the growing demand for transparent conductive films in smart and wearable electronics for electromagnetic interference (EMI) shielding, achieving a flexible EMI shielding film, while maintaining a high transmittance remains a significant challenge. Herein, a flexible, transparent, and conductive copper (Cu) metal mesh film for EMI shielding is fabricated by self-forming crackle template method and electroplating technique. The Cu mesh film shows an ultra-low sheet resistance (0.18 Ω â–¡-1), high transmittance (85.8%@550 nm), and ultra-high figure of merit (> 13,000). It also has satisfactory stretchability and mechanical stability, with a resistance increases of only 1.3% after 1,000 bending cycles. As a stretchable heater (ε > 30%), the saturation temperature of the film can reach over 110 °C within 60 s at 1.00 V applied voltage. Moreover, the metal mesh film exhibits outstanding average EMI shielding effectiveness of 40.4 dB in the X-band at the thickness of 2.5 µm. As a demonstration, it is used as a transparent window for shielding the wireless communication electromagnetic waves. Therefore, the flexible and transparent conductive Cu mesh film proposed in this work provides a promising candidate for the next-generation EMI shielding applications.

Prognostic nomogram for patients with tongue squamous cell carcinoma: A SEER-based study.

OBJECTIVES: In order to predict the patients' prognosis with tongue squamous cell carcinoma (SCC), this study set out to develop a clinically useful and trustworthy prognostic nomogram. SUBJECTS AND METHODS: The Surveillance, Epidemiology, and End Results (SEER) Program was used to compile clinical information on patients with tongue SCC between 2010 and 2015. The likelihood of Cancer-Specific Survival (CSS) and Overall Survival (OS) for specific patients was predicted using a prognostic nomogram created with the help of the RStudio software. The nomogram's predictive ability was evaluated using the consistency index (C-index) and decision curve analysis, and the nomogram was calibrated for 1-, 2-, 3-, 5-, and 10-year CSS and OS. RESULTS: Patients numbering 6453were enrolled in this study. The primary cohort (3895) and validation cohort (2558) were each randomly assigned. Sex, age, tumor-node-metastasis (TNM) stage, surgery, chemotherapy, and radiation were significant risk factors for OS, whereas age, TNM stage, surgery, chemotherapy, and radiotherapy were significant risk factors for CSS. Additionally, C-index and calibration curves indicated that the prognostic nomogram prediction and the actual observation in both cohorts would be very coherent. CONCLUSIONS: The predictive nomogram created in this study can offer patients with tongue SCC customized treatment and survival risk assessment.

Graph-guided deep hashing networks for similar patient retrieval.

With the rapid growth and widespread application of electronic health records (EHRs), similar patient retrieval has become an important task for downstream clinical decision support such as diagnostic reference, treatment planning, etc. However, the high dimensionality, large volume, and heterogeneity of EHRs pose challenges to the efficient and accurate retrieval of patients with similar medical conditions to the current case. Several previous studies have attempted to alleviate these issues by using hash coding techniques, improving retrieval efficiency but merely exploring underlying characteristics among instances to preserve retrieval accuracy. In this paper, drug categories of instances recorded in EHRs are regarded as the ground truth to determine the pairwise similarity, and we consider the abundant semantic information within such multi-labels and propose a novel framework named Graph-guided Deep Hashing Networks (GDHN). To capture correlation dependencies among the multi-labels, we first construct a label graph where each node represents a drug category, then a graph convolution network (GCN) is employed to derive the multi-label embedding of each instance. Thus, we can utilize the learned multi-label embeddings to guide the patient hashing process to obtain more informative and discriminative hash codes. Extensive experiments have been conducted on two datasets, including a real-world dataset concerning IgA nephropathy from Peking University First Hospital, and a publicly available dataset from MIMIC-III, compared with traditional hashing methods and state-of-the-art deep hashing methods using three evaluation metrics. The results demonstrate that GDHN outperforms the competitors at different hash code lengths, validating the superiority of our proposal.

Regulation Mechanism for Designing Decarbonization Pathways in the Copper Industry Toward Carbon Neutrality.

The transformation of the global power structure caused by the carbon neutrality goal will promote copper consumption. It is crucial to explore the decarbonization pathways of the copper industry to help fulfill greenhouse gas (GHG) emission reduction targets. This study utilized material flow analysis and life cycle assessment methods to investigate 12 different subscenarios based on international trade, circular economy, technology evolution, and environmental market factors. Policy combination scenario is employed to reveal the mechanism of decarbonization. The results show that refined copper consumption in China is expected to increase by 62.3% in 2060 compared to 2020. The GHG emissions of China's copper industry will reach 9.1 million tonnes (Mt) CO2e in 2060, technology evolution and environmental market are crucial for realizing carbon neutrality goal of this industry, accounting for 26.4 and 47.2% of emissions reductions, respectively, between 2020 and 2060. International trade and circular economy play important roles in the high-quality carbon peaking stage; however, imported copper and domestic secondary copper will constitute the basic supply of copper resources in China in the long run, and the comparative advantages of them will gradually weaken. Policy combination scenario can achieve the incentive synergy effect, with GHG reduced to 0.5 Mt CO2e in 2060. The enhanced application of policies such as material substitution and carbon emission trading will further promote industry to achieve net-zero GHG emission. We suggest regulating the industry's structure based on the international systemic circulation pattern and accelerating the construction of a green circular chain in the short term to achieve sustainable copper supply and high-quality carbon peaking. Promoting a high-quality technology development strategy and enhancing the environmental markets are recommended in the long term to achieve carbon neutrality.

An improved memetic algorithm to solve the energy-efficient distributed flexible job shop scheduling problem with transportation and start-stop constraints.

In the current global cooperative production environment, modern industries are confronted with intricate production plans, demanding the adoption of contemporary production scheduling strategies. Within this context, distributed manufacturing has emerged as a prominent trend. Manufacturing enterprises, especially those engaged in activities like automotive mold production and welding, are facing a significant challenge in managing a significant amount of small-scale tasks characterized by short processing times. In this situation, it becomes imperative to consider the transportation time of jobs between machines. This paper simultaneously considers the transportation time of jobs between machines and the start-stop operation of the machines, which is the first time to our knowledge. An improved memetic algorithm (IMA) is proposed to solve the multi-objective distributed flexible job shop scheduling problem (MODFJSP) with the goal of minimizing maximum completion time and energy consumption. Then, a new multi-start simulated annealing algorithm is proposed and integrated into the IMA to improve the exploration ability and diversity of the algorithm. Furthermore, a new multiple-initialization rule is designed to enhance the quality of the initial population. Additionally, four improved variable neighborhood search strategies and two energy-saving strategies are designed to enhance the search ability and reduce energy consumption. To verify the effectiveness of the IMA, we conducted extensive testing and comprehensive evaluation on 20 instances. The results indicate that, when faced with the MODFJSP, the IMA can achieve better solutions in almost all instances, which is of great significance for the improvement of production scheduling in intelligent manufacturing.

A multivariable model of ultrasound and clinicopathological features for predicting axillary nodal burden of breast cancer: potential to prevent unnecessary axillary lymph node dissection.

BACKGROUND: To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features. METHODS AND MATERIALS: Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups: limited nodal burden (0-2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors. RESULTS: Univariate logistic regression analysis revealed that the cortical thickness (p < 0.001), longitudinal to transverse ratio (p = 0.001), absence of hilum (p < 0.001), T stage (p = 0.002) and Ki-67 (p = 0.039) were significantly associated with heavy nodal burden. In the multivariate logistic regression analysis, cortical thickness (p = 0.001), absence of hilum (p = 0.042) and T stage (p = 0.012) were considered independent predictors of high-burden node. The area under curve (AUC) of the nomogram was 0.749. CONCLUSION: Our model based on US variables and clinicopathological characteristics demonstrates that can help select patients with ≥ 3 LNs, which can in turn be helpful to predict high axillary nodal burden in early breast cancer patients and prevent unnecessary axillary lymph node dissection.

CEHMR: Curriculum learning enhanced hierarchical multi-label classification for medication recommendation.

The medication recommendation (MR) or medication combination prediction task aims to predict effective prescriptions given accurate patient representations derived from electronic health records (EHRs), which contributes to improving the quality of clinical decision-making, especially for patients with multi-morbidity. Although in recent years deep learning technology has achieved great success in MR, the performance of current multi-label based MR solutions is unsatisfactory. They mainly focus on improving the patient representation module and modeling the medication label dependencies such as drug-drug interaction (DDI) correlation and co-occurrence relationship. However, the hierarchical dependency among medication labels and diversity of difficulty among MR training examples lack sufficient consideration. In this paper, we propose a framework of Curriculum learning Enhanced Hierarchical multi-label classification for MR (CEHMR). Motivated by the category hierarchy of medications which organizes standard medication codes in a hierarchical structure, we utilize it to provide more trustworthy prior knowledge for modeling label dependency. Specifically, we design a hierarchical multi-label classifier with a learnable gate fusion layer, to simultaneously capture the level-independent (local) and level-dependent (global) hierarchical information in the medication hierarchy. In addition, to overcome the diversity of training example difficulties, and progressively achieve a smoother training process, we introduce a bootstrap-based curriculum learning strategy. Hence, the example difficulty can be measured based on the predictive performance of the MR model, and then all training examples would be retrained from easy to hard under the guidance of a predefined training scheduler. Experiments on the real-world medical MIMIC-III database demonstrate that the proposed framework can achieve state-of-the-art performance compared with seven representative baselines, and extensive ablation studies validate the effectiveness of each component of CEHMR.

Growth mechanisms and anisotropic softness-dependent conductivity of orientation-controllable metal-organic framework nanofilms.

Conductive metal-organic frameworks (cMOFs) manifest great potential in modern electrical devices due to their porous nature and the ability to conduct charges in a regular network. cMOFs applied in electrical devices normally hybridize with other materials, especially a substrate. Therefore, the precise control of the interface between cMOF and a substrate is particularly crucial. However, the unexplored interface chemistry of cMOFs makes the controlled synthesis and advanced characterization of high-quality thin films, particularly challenging. Herein, we report the development of a simplified synthesis method to grow "face-on" and "edge-on" cMOF nanofilms on substrates, and the establishment of operando characterization methodology using atomic force microscopy and X-ray, thereby demonstrating the relationship between the soft structure of surface-mounted oriented networks and their characteristic conductive functions. As a result, crystallinity of cMOF nanofilms with a thickness down to a few nanometers is obtained, the possible growth mechanisms are proposed, and the interesting anisotropic softness-dependent conducting properties (over 2 orders of magnitude change) of the cMOF are also illustrated.

High Sensitivity, Wide Linear-Range Strain Sensor Based on MXene/AgNW Composite Film with Hierarchical Microcrack.

Stretchable strain sensors suffer the trade-off between sensitivity and linear sensing range. Developing sensors with both high sensitivity and wide linear range remains a formidable challenge. Different from conventional methods that rely on the structure design of sensing nanomaterial or substrate, here a heterogeneous-surface strategy for silver nanowires (AgNWs) and MXene is proposed to construct a hierarchical microcrack (HMC) strain sensor. The heterogeneous surface with distinct differences in cracks and adhesion strengths divides the sensor into two regions. One region contributes to high sensitivity through penetrating microcracks of the AgNW/MXene composite film during stretching. The other region maintains conductive percolation pathways to provide a wide linear sensing range through network microcracks. As a result, the HMC sensor exhibits ultrahigh sensitivity (gauge factor ≈ 244), broad linear range (ɛ = 60%, R2 ≈ 99.25%), and fast response time (<30 ms). These merits are confirmed in the detection of large and subtle human motions and digital joint movement for Morse coding. The manipulation of cracks on the heterogeneous surface provides a new paradigm for designing high-performance stretchable strain sensors.

Crossover Sorption of C2 H2 /CO2 and C2 H6 /C2 H4 in Soft Porous Coordination Networks.

Porous sorbents are materials that are used for various applications, including storage and separation. Typically, the uptake of a single gas by a sorbent decreases with temperature, but the relative affinity for two similar gases does not change. However, in this study, we report a rare example of "crossover sorption," in which the uptake capacity and apparent affinity for two similar gases reverse at different temperatures. We synthesized two soft porous coordination polymers (PCPs), [Zn2 (L1)(L2)2 ]n (PCP-1) and [Zn2 (L1)(L3)2 ]n (PCP-2) (L1= 1,4-bis(4-pyridyl)benzene, L2=5-methyl-1,3-di(4-carboxyphenyl)benzene, and L3=5-methoxy-1,3-di(4-carboxyphenyl)benzene). These PCPs exhibits structural changes upon gas sorption and show the crossover sorption for both C2 H2 /CO2 and C2 H6 /C2 H4 , in which the apparent affinity reverse with temperature. We used in situ gas-loading single-crystal X-ray diffraction (SCXRD) analysis to reveal the guest inclusion structures of PCP-1 for C2 H2 , CO2 , C2 H6 , and C2 H4 gases at various temperatures. Interestingly, we observed three-step single-crystal to single-crystal (sc-sc) transformations with the different loading phases under these gases, providing insight into guest binding positions, nature of host-guest or guest-guest interactions, and their phase transformations upon exposure to these gases. Combining with theoretical investigation, we have fully elucidated the crossover sorption in the flexible coordination networks, which involves a reversal of apparent affinity and uptake of similar gases at different temperatures. We discovered that this behaviour can be explained by the delicate balance between guest binding and host-guest and guest-guest interactions.

Soft corrugated channel with synergistic exclusive discrimination gating for CO2 recognition in gas mixture.

Developing artificial porous systems with high molecular recognition performance is critical but very challenging to achieve selective uptake of a particular component from a mixture of many similar species, regardless of the size and affinity of these competing species. A porous platform that integrates multiple recognition mechanisms working cooperatively for highly efficient guest identification is desired. Here, we designed a flexible porous coordination polymer (PCP) and realised a corrugated channel system that cooperatively responds to only target gas molecules by taking advantage of its stereochemical shape, location of binding sites, and structural softness. The binding sites and structural deformation act synergistically, exhibiting exclusive discrimination gating (EDG) effect for selective gate-opening adsorption of CO2 over nine similar gas molecules, including N2, CH4, CO, O2, H2, Ar, C2H6, and even higher-affinity gases such as C2H2 and C2H4. Combining in-situ crystallographic experiments with theoretical studies, it is clear that this unparalleled ability to decipher the CO2 molecule is achieved through the coordination of framework dynamics, guest diffusion, and interaction energetics. Furthermore, the gas co-adsorption and breakthrough separation performance render the obtained PCP an efficient adsorbent for CO2 capture from various gas mixtures.

Xylocopa caerulea and Xylocopa auripennis harbor a homologous gut microbiome related to that of eusocial bees.

Background: Eusocial bees, such as bumblebees and honey bees, harbor host-specific gut microbiota through their social behaviors. Conversely, the gut microbiota of solitary bees is erratic owing to their lack of eusocial activities. Carpenter bees (genus Xylocopa) are long-lived bees that do not exhibit advanced eusociality like honey bees. However, they often compete for nests to reproduce. Xylocopa caerulea and Xylocopa auripennis are important pollinators of wild plants on Hainan Island. Whether they have host-specific bacteria in their guts similar to eusocial bees remains unknown. Methods: We targeted the bacterial 16S rRNA V3-V4 region to investigate the diversity of bacterial symbionts in the fore-midgut and hindgut of two carpenter bees, X. caerulea and X. auripennis. Results: A maximum of 4,429 unique amplicon sequence variants (ASVs) were detected from all samples, belonging to 10 different phyla. X. caerulea and X. auripennis shared similar bacterial community profiles, with Lactobacillaceae, Bifidobacteriaceae, and Orbaceae being dominant in their entire guts. X. caerulea and X. auripennis harbor a highly conserved core set of bacteria, including the genera Candidatus Schmidhempelia and Bombiscardovia. These two bacterial taxa from carpenter bees are closely related to those isolated from bumblebees. The LEfSe analysis showed that Lactobacillaceae, Bifidobacteriaceae, and the genus Bombilactobacillus were significantly enriched in the hindguts of both carpenter bees. Functional prediction suggested that the most enriched pathways were involved in carbohydrate and lipid metabolism. Conclusions: Our results revealed the structure of the gut microbiota in two carpenter bees and confirmed the presence of some core bacterial taxa that were previously only found in the guts of social bees.

Detection of volatile organic compounds in adulterated tea using Fourier transform infrared spectroscopy and Proton-transfer-reaction mass spectrometry.

Aroma is a key factor used to evaluate tea quality. Illegal traders usually add essence to expired or substandard tea to improve its aroma so as to gain more profit. Traditional physical and chemical testing methods are time-consuming and costly. Furthermore, rapid detection techniques, such as near-infrared spectroscopy and machine vision, can only be used to detect adulterated powdered solid essences in tea. In this study, proton-transfer reaction mass spectrometry (PTR-MS) and Fourier-transform infrared spectroscopy (FTIR) were employed to detect volatile organic compounds (VOCs) in samples, and rapid detection of different tea adulterated liquid essence was achieved. The prediction accuracies of PTR-MS and FTIR reached over 0.941 and 0.957, respectively, and the minimum detection limits were lower than the actual used values in both. In this study, the different application scenarios of the two technologies are discussed based on their performance characteristics.

Gut microbiota composition and gene expression changes induced in the Apis cerana exposed to acetamiprid and difenoconazole at environmentally realistic concentrations alone or combined.

Apis cerana is an important pollinator of agricultural crops in China. In the agricultural environment, A. cerana may be exposed to acetamiprid (neonicotinoid insecticide) and difenoconazole (triazole fungicide), alone or in combination because they are commonly applied to various crops. At present, our understanding of the toxicological effects of acetamiprid and difenoconazole on honey bee gut microbiomes is limited. The primary objective of this study was to explore whether these two pesticides affect honey bees' gut microbiota and to analyze the transcriptional effects of these two pesticides on honey bees' head and gut. In this study, adults of A. cerana were exposed to acetamiprid and/or difenoconazole by contaminated syrup at field-realistic concentrations for 10 days. Results indicated that acetamiprid and/or difenoconazole chronic exposure did not affect honey bees' survival and food consumption, whereas difenoconazole decreased the weight of honey bees. 16S rRNA sequencing suggested that difenoconazole and the mixture of difenoconazole and acetamiprid decreased the diversity index and shaped the composition of gut bacteria microbiota, whereas acetamiprid did not impact the gut bacterial community. The ITS sequence data showed that neither of the two pesticides affected the fungal community structure. Meanwhile, we also observed that acetamiprid or difenoconazole significantly altered the expression of genes related to detoxification and immunity in honey bees' tissues. Furthermore, we observed that the adverse effect of the acetamiprid and difenoconazole mixture on honey bees' health was greater than that of a single mixture. Taken together, our study demonstrates that acetamiprid and/or difenoconazole exposure at field-realistic concentrations induced changes to the honey bee gut microbiome and gene expression.

Integrated Soft Porosity and Electrical Properties of Conductive-on-Insulating Metal-Organic Framework Nanocrystals.

A one-stone, two-bird method to integrate the soft porosity and electrical properties of distinct metal-organic frameworks (MOFs) into a single material involves the design of conductive-on-insulating MOF (cMOF-on-iMOF) heterostructures that allow for direct electrical control. Herein, we report the synthesis of cMOF-on-iMOF heterostructures using a seeded layer-by-layer method, in which the sorptive iMOF core is combined with chemiresistive cMOF shells. The resulting cMOF-on-iMOF heterostructures exhibit enhanced selective sorption of CO2 compared to the pristine iMOF (298 K, 1 bar, S CO 2 / H 2 ${{_{{\rm CO}{_{2}}/{\rm H}{_{2}}}}}$ from 15.4 of ZIF-7 to 43.2-152.8). This enhancement is attributed to the porous interface formed by the hybridization of both frameworks at the molecular level. Furthermore, owing to the flexible structure of the iMOF core, the cMOF-on-iMOF heterostructures with semiconductive soft porous interfaces demonstrated high flexibility in sensing and electrical "shape memory" toward acetone and CO2 . This behavior was observed through the guest-induced structural changes of the iMOF core, as revealed by the operando synchrotron grazing incidence wide-angle X-ray scattering measurements.

Effect of short-term prehabilitation of older patients with colorectal cancer: A propensity score-matched analysis.

Objective: The aim of this study was to assess the impact of short-term, hospital-based, supervised multimodal prehabilitation on elderly patients with colorectal cancer. Methods: A single-center, retrospective study was conducted from October 2020 to December 2021, which included a total of 587 CRC patients who were scheduled to undergo radical resection. A propensity score-matching analysis was performed to reduce selection bias. All patients were treated within a standardized enhanced recovery pathway, and patients in the prehabilitation group received an additional supervised, short-term multimodal preoperative prehabilitation intervention. Short-term outcomes were compared between the two groups. Results: Among the participants, 62 patients were excluded; 95 participants were included in the prehabilitation group and 430 in the non-prehabilitation group. After PSM analysis, 95 pairs of well-matched patients were included in the comparative study. Participants in the prehabilitation group had better preoperative functional capacity (402.78 m vs. 390.09 m, P<0.001), preoperative anxiety status (9% vs. 28%, P<0.001), time to first ambulation[25.0(8.0) hours vs. 28.0(12.4) hours, P=0.008], time to first flatus [39.0(22.0) hours vs. 47.7(34.0) hours, P=0.006], duration of the postoperative length of hospital stay [8.0(3.0) days vs. 10.0(5.0) days, P=0.007), and quality of life in terms of psychological dimensions at 1 month postoperatively [53.0(8.0) vs. 49.0(5.0), P<0.001]. Conclusion: The short-term, hospital-based, supervised multimodal prehabilitation is feasible with a high degree of compliance in older CRC patients, which improves their short-term clinical outcomes.

Associations between smoke exposure and osteoporosis or osteopenia in a US NHANES population of elderly individuals.

Background: Tobacco exposure is considered to be a risk factor for reduced bone mineral density (BMD), which may result in osteopenia. Cotinine, a metabolite of nicotine, is commonly utilized as a marker of tobacco exposure. Nevertheless, there are limited clinical data on the associations between osteoporosis (OP) or osteopenia and smoking status or serum cotinine level. Methods: We thoroughly examined the NHANES cross-sectional data from 2005 to 2010, 2013 to 2014, and 2017 to 2018. Multivariate logistic regression models were applied to assess the associations among smoking status and serum cotinine levels as well as OP and osteopenia. The relationships between serum cotinine level and OP and osteopenia were also assessed using the restricted cubic spline (RCS) method. Results: A total of 10,564 participants were included in this cross-sectional study. The mean age of the study population was 64.85 ± 9.54 years, and the patients were predominantly male (51.9%). We found that the relationships between higher serum cotinine levels (≥3 ng/ml) and the prevalence of osteoporosis (Model 1: OR=2.27 [1.91-2.69]; Model 2: OR=2.03 [1.70-2.43]; Model 3: OR=2.04 [1.70-2.45]; all p for trend <0.001) remained significant after adjustment for covariates by applying the lowest serum cotinine levels (<0.05 ng/ml) as the reference. Similar results were observed for current smokers, who were more likely to develop OP compared with nonsmokers (Model 1: OR=2.30 [1.90-2.79]; Model 2: OR=2.16 [1.77-2.64]; Model 3: OR=2.16 [1.77-2.65]). Moreover, higher serum cotinine levels were found to be strongly and positively correlated with the prevalence of osteopenia (OR=1.60 [1.42-1.80]). A similar relationship was observed between current smokers and the prevalence of osteopenia compared with nonsmokers (OR=1.70 [1.49-1.94]). RCS regression also showed that serum cotinine levels were nonlinearly and positively correlated with OP and osteopenia, with inflection points of 5.82 ng/ml and 3.26 ng/ml, respectively. Conclusion: This study showed that being a smoker was associated with the prevalence of OP or osteopenia compared with being a nonsmoker and that there was a strong nonlinear positive dose-response relationship between serum cotinine levels and OP and osteopenia.