[Establishment and evaluation of a rabbit model of frozen shoulder induced by persistent strain injuries and ice compression].

OBJECTIVE: To evaluate the rabbit modle of frozen shoulder induced by persistent strain injuries and ice compression. METHODS: Twelve clean, healthy male New Zealand rabbits with a mass of (2 500±500) g were selected and randomly divided into a blank group and a control group with 6 rabbits in each group. In the control group, the rabbits were modeled with persistent strain injuries and ice compression, the general conditions of the rabbits and the active and passive activities of the shoulder joint were observed and their body weights were recorded. MRI was performed on the affected shoulder joints at 6 d and 29 d after modelling to observe the fluid and soft tissue;HE staining was used to observe the morphology of the rabbit biceps longus tendon and the synovial membrane of the joint capsule;Masson staining was used to observe the fibrous deposits of the rabbit biceps longus tendon and the synovial membrane of the joint capsule, and the fibrous deposits were analysed semi-quantitatively by Image J software. RESULTS: Six days after the end of modeling, the active movement of the shoulder joints in the control group was limited, the passive movement was not significantly limited, and they walked with a limp;29 days after the end of the modeling, the active and passive movements of the shoulder joints in the model group were severely limited. Compared with the blank group (2.50±0.14) kg, the body weight of the model group (2.20±0.17) kg was significantly reduced(P<0.01). MRI showed that 6 days after modelling, the muscles around the shoulder joint were not smooth in shape, the joint capsule structure was narrowed and a large amount of fluid was seen in the joint cavity;29 days after modelling, the muscles around the shoulder joint were rough in shape, structure of the joint capsule was unclear and the fluid in the joint cavity was reduced compared with 6 days after modelling. Pathological staining showed that the long-headed biceps tendon fibres in the control group were disorganised, curled or even broken, and the synovial tissue of the joint capsule was heavily vascularised, with collagen fibre deposits and severe inflammatory cell infiltration. The fiber deposition of the long head of biceps brachii in the model group [(23.58±3.41)%, (27.56±3.70)%] and synovial tissue [(41.78±5.59)%, (62.19±7.54)%] were significantly higher than those in the blank group [(1.79±1.03) %, (1.29±0.63) %] at 7 and 30 days after modeling and synovial tissue fiber deposition [(8.15±3.61) %, (11.29±7.10) %], as shown by the semi-quantitative analysis of Masson staining results by Image J software. And the longer the time, the more severe the fibrosis (P<0.01). CONCLUSION: The behavioral, imaging and pathological findings showed that the rabbit frozen shoulder model with persistent strain injuries and ice compression is consistent with the clinical manifestations and pathogenesis of periarthritis, making it an ideal method for periarthritis research.

Parent material influences soil properties to shape bacterial community assembly processes, diversity, and enzyme-related functions.

Soil parent material is the second most influential factor in pedogenesis, influencing soil properties and microbial communities. Different assembly processes shape diverse functional microbial communities. The question remains unresolved regarding how these ecological assembly processes affect microbial communities and soil functionality within soils on different parent materials. We collected soil samples developed from typical parent materials, including basalt, granite, metamorphic rock, and marine sediments across soil profiles at depths of 0-20, 20-40, 40-80, and 80-100 cm, within rubber plantations on Hainan Island, China. We determined bacterial community characteristics, community assembly processes, and soil enzyme-related functions using 16S rRNA high-throughput sequencing and enzyme activity analyses. We found homogeneous selection, dispersal limitation, and drift processes were the dominant drivers of bacterial community assembly across soils on different parent materials. In soils on basalt, lower pH and higher moisture triggered a homogeneous selection-dominated assembly process, leading to a less diverse community but otherwise higher carbon and nitrogen cycling enzyme activities. As deterministic process decreased, bacterial community diversity increased with stochastic process. In soils on marine sediments, lower water, carbon, and nutrient content limited the dispersal of bacterial communities, resulting in higher community diversity and an increased capacity to utilize relative recalcitrant substrates by releasing more oxidases. The r-strategy Bacteroidetes and genera Sphingomonas, Bacillus, Vibrionimonas, Ochrobactrum positively correlated with enzyme-related function, whereas k-strategy Acidobacteria, Verrucomicrobia and genera Acidothermus, Burkholderia-Caballeronia-Paraburkholderia, HSB OF53-F07 showed negative correlations. Our study suggests that parent material could influence bacterial community assembly processes, diversity, and soil enzyme-related functions via soil properties.

Research on distributionally robust energy storage capacity allocation for output fluctuations in high permeability wind and solar distribution networks.

This paper presents a novel approach to addressing the challenges associated with energy storage capacity allocation in high-permeability wind and solar distribution networks. The proposed method is a two-phase distributed robust energy storage capacity allocation method, which aims to regulate the stochasticity and volatility of net energy output. Firstly, an energy storage capacity allocation model is established, which considers energy storage's investment and operation costs to minimize the total cost. Then, a two-stage distributed robust energy storage capacity allocation model is established with the confidence set of uncertainty probability distribution constrained by 1-norm and ∞-norm. Finally, a Column and Constraint Generation (C&CG) algorithm is used to solve the problem. The validity of the proposed energy storage capacity allocation model is confirmed by examining different wind and solar penetration levels. Furthermore, the model's superiority is demonstrated by comparing it with deterministic and robust models.

Effector enrichment by Candidatus Liberibacter promotes Diaphorina citri feeding via Jasmonic acid pathway suppression.

BACKGROUND: Citrus huanglongbing (HLB) is a devastating disease caused by Candidatus Liberibacter asiaticus (CLas) that affects the citrus industry. In nature, CLas relies primarily on Diaphorina citri Kuwayama as its vector for dissemination. After D. citri ingests CLas-infected citrus, the pathogen infiltrates the insect's body, where it thrives, reproduces, and exerts regulatory control over the growth and metabolism of D. citri. Previous studies have shown that CLas alters the composition of proteins in the saliva of D. citri, but the functions of these proteins remain largely unknown. RESULTS: In this study, we detected two proteins (DcitSGP1 and DcitSGP3) with high expression levels in CLas-infected D. citri. Quantitative PCR and Western blotting analysis showed that the two proteins were highly expressed in the salivary glands and delivered into the host plant during feeding. Silencing the two genes significantly decreased the survival rate for D. citri, reduced phloem nutrition sucking and promoted jasmonic acid (JA) defenses in citrus. By contrast, after overexpressing the two genes in citrus, the expression levels of JA pathway-associated genes decreased. CONCLUSION: Our results suggest that CLas can indirectly suppress the defenses of citrus and support feeding by D. citri via increasing the levels of effectors in the insect's saliva. This discovery facilitates further research into the interaction between insect vectors and pathogens. © 2024 Society of Chemical Industry.

The multimodality cell segmentation challenge: toward universal solutions.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

[Identifying animal-derived components in camel milk and its products by ultra-high performance liquid chromatography-tandem mass spectrometry].

A method for identifying specific peptide biomarkers of animal-milk-derived components in camel milk and its products was established using proteomics. Samples were prepared by defatting, protein extraction, and trypsin hydrolysis, and proteins and peptides were identified using ultra-high performance liquid chromatography-quadrupole/electrostatic orbitrap-high resolution mass spectrometry (UHPLC-Q/Exactive-HRMS) and Protein Pilot software. Twenty two peptide biomarkers from eight species (i.e., Camelus, Bos taurus, Bubalus bubalis, Bos grunniens/Bos mutus, Capra hircus, Ovis aries, Equus asinus, Equus caballus) were identified by comparing the basic local alignment search tool (BLAST) with the Uniprot database. Verification of these marker peptides were performed quantitatively using a UHPLC-triple-quadrupole mass-spectrometry (QqQ-MS) system by multiple reaction monitoring (MRM). The pretreatment method of casein in camel milk was optimized, such as defatting, protein precipitation, and re-dissolving buffer solution. The effects of various mass-spectrometry parameters, such as atomization gas, heating- and drying-gas flow rates, and desolvation-tube (DL) and ion-source-interface temperatures on ion-response intensity were optimized. Camel milk signature peptides were detected in a mixture of milk from other seven species to ensure specificity for the selected biomarker peptides. The signature peptides of seven other species were also detected in camel milk. No mutual interference between the selected biomarker peptides of the various species was observed. Adulterated camel milk and milk powder were also quantitatively studied by adding 0, 2.5%, 5%, 10%, 25%, 50%, 75%, and 100% bovine milk or goat milk to camel milk. Similarly, the same mass proportion of bovine milk powder or goat milk powder was added to camel milk powder. A quantitative standard curve for adulteration was constructed by plotting the peak areas of characteristic cow or goat peptide segments in each mixed sample against the mass percentage of the added adulterant. The adulteration standard curves exhibited good linearity, with correlation coefficients (r2) greater than 0.99. The limits of detection and quantification (LODs and LOQs, respectively) of the method were determined as three- and ten-times the signal-to-noise ratio (S/N). The minimum adulteration LODs of bovine milk and goat milk in camel milk were determined to be 0.35% and 0.49%, respectively, and the minimum LOQs were 1.20% and 1.69%, respectively. The minimum adulteration LODs of bovine milk powder and goat milk powder in camel milk powder were determined to be 0.68% and 0.73%, respectively, and the minimum LOQs were 1.65% and 2.45%, respectively. The accuracy of the adulteration quantification method was investigated by validating the quantitative detection results for 1∶1∶1 (mass ratio) mixtures of camel milk, bovine milk, and goat milk, as well as camel-milk powder, bovine milk powder, and goat-milk powder, which revealed that this method exhibits good linearity, strong anti-interference, high sensitivity, and good repeatability for adulterated liquid-milk/solid-milk-powder samples. The adulteration results for both liquid milk and milk powder are close to the theoretical values. Finally, 11 actual commercially available samples, including five camel-milk and six camel-milk-powder samples were analyzed, which revealed that only camel signature peptides were detected in 10 samples, while camel and bovine signature peptides were both detected in one camel-milk-powder sample. The ingredient list of the latter sample revealed that it contained whole milk powder from an unidentified source; therefore, we infer that the bovine signature peptides originate from the whole milk powder. These signature peptides also demonstrate the necessity and practical significance of establishing this identification method.

Expression, Prognostic Value, and Immune Infiltration of MTHFD Family in Bladder Cancer.

BACKGROUND: The Methylenetetrahydrofolate Dehydrogenase (MTHFD) family plays an important role in the development and prognosis of a variety of tumors; however, the role of the MTHFD family in bladder cancer is unclear. METHODS: R software, cBioPortal, GeneMANIA, and online sites such as String-LinkedOmics were used for bioinformatics analysis. RESULTS: MTHFD1/1L/2 was significantly upregulated in bladder cancer tissues compared with normal tissues, high expression of the MTHFD family was strongly associated with poorer clinical grading and staging, and bladder cancer patients with upregulated expression of MTHFD1L/2 had a significantly worse prognosis. Gene function and PPI network analysis revealed that the MTHFD family and related genes play synergistic roles in the development of bladder cancer. 800 co-expressed genes related to the MTHFD family were used for functional enrichment analysis, and the results showed that many genes were associated with various oncogenic pathways such as cell cycle and DNA replication. More importantly, the MTHFD family was closely associated with multiple infiltrating immune lymphocytes, including Treg cells, and immune molecules such as TNFSF9, CD274, and PDCD1. CONCLUSION: Our study shows that MTHFD family genes may be potential prognostic markers and therapeutic targets for patients with bladder cancer.

Spine MRI image segmentation method based on ASPP and U-Net network.

The spine is one of the most important structures in the human body, serving to support the body, organs, protect nerves, etc. Medical image segmentation for the spine can help doctors in their clinical practice for rapid decision making, surgery planning, skeletal health diagnosis, etc. The current difficulty is mainly the poor segmentation accuracy of skeletal Magnetic Resonance Imaging (MRI) images. To address the problem, we propose a spine MRI image segmentation method, Atrous Spatial Pyramid Pooling (ASPP)-U-shaped network (UNet), which combines an ASPP structure with a U-Net network. This approach improved the network feature extraction by introducing an ASPP structure into the U-Net network down-sampling structure. The medical image segmentation models are trained and tested on publicly available datasets and obtained the Dice coefficient and Mean Intersection over Union coefficients with 0.866 and 0.755, respectively. The experimental results show that ASPP-UNet has higher accuracy for spine MRI image segmentation compared with other mainstream networks.

Antibody sequences assembly method based on weighted de Bruijn graph.

With the development of next-generation protein sequencing technologies, sequence assembly algorithm has become a key technology for de novo sequencing process. At present, the existing methods can address the assembly of an unknown single protein chain. However, for monoclonal antibodies with light and heavy chains, the assembly is still an unsolved question. To address this problem, we propose a new assembly method, DBAS, which integrates the quality scores and sequence alignment scores from de novo sequencing peptides into a weighted de Bruijn graph to assemble the final protein sequences. The established method is used to assembling sequences from two datasets with mixed light and heavy chains from antibodies. The results show that the DBAS can assemble long antibody sequences for both mixed light and heavy chains and single chains. In addition, DBAS is able to distinguish the light and heavy chains by using BLAST sequence alignment. The results show that the algorithm has good performance for both target sequence coverage and contig assembly accuracy.

Effect of ß-blockers on mortality in patients with sepsis: A propensity-score matched analysis.

Objectives: We aimed to evaluate the association between ß-blocker therapy and mortality in patients with sepsis. Methods: Patients with sepsis were selected from the Medical Information Mart for Intensive Care (MIMIC)-III. Propensity score matching (PSM) was used to balance the baseline differences. A multivariate Cox regression model was used to assess the relationship between ß-blocker therapy and mortality. The primary outcome was the 28-day mortality. Results: A total of 12,360 patients were included in the study, involving 3,895 who received ß-blocker therapy and 8,465 who did not. After PSM, 3,891 pairs of patients were matched. The results showed that ß-blockers were associated with improved 28- (hazards ratio (HR) 0.78) and 90-day (HR 0.84) mortality. Long-acting ß-blockers were associated with improved 28-day survival (757/3627 [20.9%] vs. 583/3627 [16.1%], P < 0.001, HR0.76) and 90-day survival (1065/3627 [29.4%] vs.921/3627 [25.4%], P < 0.001, HR 0.77). Short-acting ß-blocker treatment did not reduce the 28-day and 90-day mortality (61/264 [23.1%] vs. 63/264 [23.9%], P = 0.89 and 83/264 [31.4%] vs. 89/264 [31.7%], P = 0.8, respectively). Conclusions: ß-blockers were associated with improved 28- and 90-day mortality in patients with sepsis and septic shock. Long-acting ß-blocker therapy may have a protective role in patients with sepsis, reducing the 28-day and 90-day mortality. However, short-acting ß-blocker (esmolol) treatment did not reduce the mortality in sepsis.

Incidence, Prognostic Factors, and Survival of Patients with Renal Cancer: A Population-Based Study.

BACKGROUND: The main objective of this article is to understand trends in the incidence of renal cancer and to construct a nomogram to predict the prognosis of patients with renal cancer by analyzing clinical parameters. METHODS: We extracted data from the Surveillance, Epidemiology and End Results (SEER) database for patients with renal cancer from 2010 to 2015. The incidence rate was calculated to understand the trend of renal cancer in recent years, and the Kaplan-Meier method was used to analyze the relationship between patients' clinical variables and overall survival. Nomogram and calibration curves were constructed based on factors predicted by multivariate Cox regression. RESULTS: Data from 68,496 eligible renal cancer patients were included in the study. The incidence of renal cancer was higher in men than women and tended to stabilize over time. We further found that age, gender, marital status, AJCC stage, histological type, metastatic disease, and surgery were independent parameters for prognosis in renal cancer patients. Finally, a nomogram was constructed based on the above parameters, and its validity was verified with the agreement index and calibration curve. CONCLUSION: Renal cancer incidence trend gradually stabilized. Seven independent parameters for renal cancer patients were obtained by analysis and utilized to construct a nomogram that could provide guidance for clinical practice.

Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure-activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4-8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.

Optical trapping force on a stratified chiral particle by high-order Bessel beam.

We examined optical trapping force (TF) exerted on non-uniform chiral stratified spheres by a high-order Bessel beam (HOBB). Present theories were proven to be valid by comparison with the existing reference. Numerical simulations considering the effects of various parameters on TF are displayed in detail. The results show that different chirality distributions in stratified chiral sphere will affect significantly the trapping characteristics, and a stable three-dimensional capture can be realized only by selecting the appropriate parameters of incident beam and particles. The theoretical investigations may provide an analytical method to help understand the interaction of light with more complex stratified chiral cells and thus become an encouraging approach to better design an optical manipulation system.

Hippocampus-prefrontal cortex inputs modulate spatial learning and memory in a mouse model of sepsis induced by cecal ligation puncture.

AIMS: Sepsis-associated encephalopathy (SAE) often leads to cognitive impairments. However, the pathophysiology of SAE is complex and unclear. Here, we investigated the role of hippocampus (HPC)-prefrontal cortex (PFC) in cognitive dysfunction in sepsis induced by cecal ligation puncture (CLP) in mice. METHODS: The neural projections from the HPC to PFC were first identified via retrograde tracing and viral expression. Chemogenetic activation of the HPC-PFC pathway was shown via immunofluorescent staining of c-Fos-positive neurons in PFC. Morris Water Maze (MWM) and Barnes maze (BM) were used to evaluate cognitive function. Western blotting analysis was used to determine the expression of glutamate receptors and related molecules in PFC and HPC. RESULTS: Chemogenetic activation of the HPC-PFC pathway enhanced cognitive dysfunction in CLP-induced septic mice. Glutamate receptors mediated the effects of HPC-PFC pathway activation in CLP mice. The activation of the HPC-PFC pathway resulted in significantly increased levels of NMDAR, AMPAR, and downstream signaling molecules including CaMKIIa, pCREB, and BDNF in PFC. However, inhibition of glutamate receptors using 2,3-dihydroxy-6-nitro-7-sulphamoyl-benzo (F)quinoxaline (NBQX), which is an α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR inhibitor), or D-2-amino-5-phosphonopentanoate (D-AP5), which is an NMDA receptor antagonist abolished this increase. CONCLUSION: Our study reveals the important role of the HPC-PFC pathway in improving cognitive dysfunction in a mouse model of CLP sepsis and provides a novel pathogenetic mechanism for SAE.

Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks.

Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.

Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge.

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.

Incidence, prognostic factors and survival in bladder cancer patients: a population-based study.

Background: The aim of this study was to investigate the incidence, epidemiologic characteristics, prognostic factors and survival of patients with bladder cancer. Methods: Bladder cancer patients diagnosed between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox proportional hazards regression analyses were used to identify the independent prognostic factors for overall survival. Kaplan-Meier survival analysis and nomogram analysis were constructed based on the identified independent prognostic factors. Results: A total of 95,329 eligible bladder cancer patients were included in this study. Eight independent risk factors, including age, histologic type, race, tumor, node and metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, surgery, tumor metastasis and summary stage, were recognized by using multivariate logistic regression models. By comprising these factors, a predictive nomogram was constructed to predict the 1-, 3-, and 5-year overall survival possibilities. The concordance index and calibration curve showed that the nomogram had robust and accurate performance. Conclusions: Bladder cancer is the most common cancer of the urinary system, but the overall incidence has been decreasing yearly since 1992. Our results demonstrate eight factors significantly associated with overall survival in bladder cancer patients. Based on these factors, we established and validated a nomogram, which has the potential to provide an individualized prediction of overall survival in patients with bladder cancer.

Compassionate Use of Contezolid for the Treatment of Tuberculous Pleurisy in a Patient with a Leadless Pacemaker.

We report the case of an 87-year-old woman with tuberculous pleurisy. She developed adverse effects in the form of thrombocytopenia and gastrointestinal hemorrhage with isoniazid, and thrombocytopenia with linezolid. Her treatment was switched to contezolid plus cycloserine for a 4-week antibiotic duration, with a favorable outcome.

Perfluorooctanoic acid (PFOA)-induced alterations of biomolecules in the wetland plant Alismaorientale.

Perfluoroalkyl substances (PFASs) have been widely studied by researchers due to their environmental persistence, chemical stability and potential toxicity. Some researchers have reported the physiological and biochemical toxicity of PFASs on plants through traditional and innovative methods; however, the changes in biological macromolecules caused by PFASs are rarely studied. Here, Fourier transform infrared spectroscopy (FTIR) was used to study how exposure to perfluorooctanoic acid (PFOA) alters the structure and function of biomolecules of the wetland plant Alisma orientale. Biomass results showed that PFOA had negative effects on plant growth. FTIR results showed that PFOA could result in changes in the structures, compositions, and functions of lipids, proteins and DNA in plant cells. In the treatment groups, the ratios of CH3 to lipids and carbonyl esters to lipids increased compared with the control, while the ratios of CH2 to lipids and olefinicCH to lipids decreased, which indicated lipid peroxidation caused by PFOA exposure. Changes in the compositions and secondary structures of proteins were also found, which were indicated by the decreased ratio of amide I to amide II and the increased ratio of ß-sheet to α-helix in the treatment groups compared to the control. Moreover, PFOA affected the composition of DNA by promoting the B- to A-DNA transition. These results showed that the mechanism of PFOA toxicity toward plants at the biochemical level could be illustrated by FTIR.

Distribution Characteristics of Colorectal Peritoneal Carcinomatosis Based on the Positron Emission Tomography/Peritoneal Cancer Index.

Background: Colorectal peritoneal carcinomatosis (CRPC) is a primary cause of death in colorectal cancer (CRC) patients. In the past, computed tomography (CT) has been the primary method used to evaluate the distribution of CRPC. This study uses 18F-FDG positron emission tomography/computed tomography (PET/CT) to investigate the distribution characteristics of CRPC. Materials and Methods: The distribution characteristics of 46 patients with CRC who were treated in the authors' hospital were retrospectively analyzed using the peritoneal cancer index (PCI). Results: The 46 patients in the study showed CRPC involvement in 203 of the 598 abdominal and pelvic regions studied (33.9%, 203/598). The regional proportions of CRPC involvement, from high to low, were as follows: region 6 (13.8%), region 0 (10.3%), region 1 (9.9%), region 5 (8.9%), region 7 (8.4%), region 3 (8.4%), region 2 (7.4%), region 4 (7.4%), region 11 (6.9%), region 8 (6.4%), region 12 (5.4%), region 9 (3.4%), and region 10 (3.4%). Thirty-three patients had a PCI of <20, and 13 patients had a PCI of ≥20. Those 13 were among the 17 (37% 17/46) who had CRPC involvement in all three regions. According to the location of the primary CRC focus, the 46 patients were divided into three groups: right hemicolon, left hemicolon, and rectum. The frequency of CRPC was greater in the rectum group than in the left hemicolon group, and the SUVmax of CRPC was greater in the right hemicolon group than in the left hemicolon group; these differences were statistically significant (p < 0.05). Conclusions: The distribution of CRPC has certain characteristics in the abdominal and pelvic cavities. The PET-PCI scores can provide a basis for the diagnosis and clinical treatment strategies in patients with CRC.