Analysis of Distribution and Drug Susceptibility Test Results of Pathogenic Bacteria in Diabetic Foot Ulcers.

INTRODUCTION: This study aimed to determine the pathogen distribution and drug susceptibility of diabetic foot wound secretions in a tertiary hospital in a coastal area of southeastern China to guide clinical antibiotic selection. METHODS: A retrospective analysis was conducted on 212 patients with diabetic foot hospitalized at Xiamen Third Hospital from 2018 to 2023, and foot wound secretions were collected for microbial culture and drug susceptibility testing. RESULTS: Among 212 cases of patients with diabetic foot wound secretions, 163 cases (76.9%) were cultured with pathogenic bacteria, and a total of 207 strains of pathogenic bacteria were cultured, including 75 strains (36.23%) of Gram-positive (G+) bacteria, 118 strains of Gram-negative (G-) bacteria (57.00%), 14 strains of fungi (6.76%), 120 cases of single microorganism infection (73.62%), 43 cases of mixed infection (26.38%), and 15 strains of multidrug-resistant bacteria (7.25%). The top three pathogenic bacteria were Staphylococcus aureus, Klebsiella pneumoniae, and Pseudomonas aeruginosa. G+ bacteria were dominated by S. aureus. Drug susceptibility results showed that G+ bacteria were highly susceptible to vancomycin, linezolid, tigecycline, quinupristin/dalfopristin, rifampicin, and furotoxin, and somewhat resistant to penicillin, erythromycin, clindamycin, and cefoxitin. Among G- bacterial infections, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, and Proteus were the major species. Drug susceptibility testing indicated that carbapenems such as imipenem and ertapenem were the most effective antibacterial drugs against G- strains, followed by amikacin, piperacillin, and tazabactams to which these bacteria were also relatively sensitive, while resistance to penicillins and first-generation cephalosporins increased significantly. We isolated one strain of pathogenic bacteria from a Wagner grade 1 ulcer, which was G+ bacteria. In Wagner grade 2 ulcers, the distribution of pathogenic bacteria was mainly G+ bacteria. In Wagner grade 3 and 4 ulcers, the distribution of pathogenic bacteria was mainly G- bacteria, and the increased rate of mixed infection was mainly due to mixed infection of G+ and G-. Two strains of pathogenic bacteria were isolated at Wagner grade 5, which were mixed infections of G+ and G-. CONCLUSIONS: Pathogenic bacteria in diabetic foot wounds are predominantly G- bacteria, followed by G+ bacteria. As the Wagner ulcer grade increases, the distribution of pathogenic bacteria changes from G+ bacteria to G- bacteria, and the mixed infection rate increases. G+ bacteria are highly susceptible to vancomycin, linezolid, tigecycline, quinupristin/dalfopristin, rifampicin, and furotoxin, and somewhat resistant to penicillin, erythromycin, clindamycin, and cefoxitin. G- bacteria are more sensitive to the antimicrobial drugs ertapenem, imipenem, amikacin, piperacillin tazobactam, and have high resistance to penicillin and first-generation cephalosporins.

A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes.

RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes. MATERIALS AND METHODS: This retrospective study included 366 breast cancer patients from two institutes, divided into training (n = 292), validation (n = 49) and testing (n = 25) sets. We first transformed the public DCE-MRI appearance to ours to alleviate small-data-size and class-imbalance issues. Second, we developed a multi-branch convolutional-neural-network (MBCNN) to perform molecular subtype prediction. Third, we assessed the MBCNN with different regions of interest (ROIs) and fusion strategies, and compared it to previous DL models. Area under the curve (AUC) and accuracy (ACC) were used to assess different models. Delong-test was used for the comparison of different groups. RESULTS: MBCNN achieved the optimal performance under intermediate fusion and ROI size of 80 pixels with appearance transformation. It outperformed CNN and convolutional long-short-term-memory (CLSTM) in predicting luminal B, HER2-enriched and TN subtypes, but without demonstrating statistical significance except against CNN in TN subtypes, with testing AUCs of 0.8182 vs. [0.7208, 0.7922] (p=0.44, 0.80), 0.8500 vs. [0.7300, 0.8200] (p=0.36, 0.70) and 0.8900 vs. [0.7600, 0.8300] (p=0.03, 0.63), respectively. When predicting luminal A, MBCNN outperformed CNN with AUCs of 0.8571 vs. 0.7619 (p=0.08) without achieving statistical significance, and is comparable to CLSTM. For four-subtype prediction, MBCNN achieved an ACC of 0.64, better than CNN and CLSTM models with ACCs of 0.48 and 0.52, respectively. CONCLUSION: Developed DL model with the feature extraction and fusion of DCE-MRI from two institutes enabled preoperative prediction of breast cancer molecular subtypes with high diagnostic performance.

QDPR deficiency drives immune suppression in pancreatic cancer.

The relevance of biopterin metabolism in resistance to immune checkpoint blockade (ICB) therapy remains unknown. We demonstrate that the deficiency of quinoid dihydropteridine reductase (QDPR), a critical enzyme regulating biopterin metabolism, causes metabolite dihydrobiopterin (BH2) accumulation and decreases the ratio of tetrahydrobiopterin (BH4) to BH2 in pancreatic ductal adenocarcinomas (PDACs). The reduced BH4/BH2 ratio leads to an increase in reactive oxygen species (ROS) generation and a decrease in the distribution of H3K27me3 at CXCL1 promoter. Consequently, myeloid-derived suppressor cells are recruited to tumor microenvironment via CXCR2 causing resistance to ICB therapy. We discovered that BH4 supplementation is capable to restore the BH4/BH2 ratio, enhance anti-tumor immunity, and overcome ICB resistance in QDPR-deficient PDACs. Tumors with lower QDPR expression show decreased responsiveness to ICB therapy. These findings offer a novel strategy for selecting patient and combining therapies to improve the effectiveness of ICB therapy in PDAC.

Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography.

Objective.Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems.Approach.Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent algorithm is introduced to solve the problem.Main results.Numerical simulation andin vivoexperimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs.Significance.Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism.

Comprehension of Ditransitive Constructions in Mandarin-Speaking Children with Developmental Language Disorder and Children with Autism Spectrum Disorder Plus Language Impairment.

This study examined and compared the comprehension of Mandarin ditransitive constructions in children with developmental language disorder (DLD) and children with autism spectrum disorder plus language impairment (ALI). Eighteen children with DLD, 17 children with ALI, and 27 age-matched typically developing (TDA) children, participated in a sentence-picture matching task on four patterns of Mandarin ditransitive constructions. Both children with DLD and children with ALI received significantly lower accuracy than TDA children in general and their most common errors were thematic role reversals. However, while children with ALI evinced a generalized deficit in all four patterns, only the comprehension of S1 (Subj. + Vgei + IO + DO) and S3 (Subj. + gei + IO + V + DO) was affected in children with DLD, with that of S2 (Subj. + V + DO + gei + IO) and S4 (Subj. + V + IO + DO) preserved in this population. Additionally, thematic role reversal errors were more dominant in children with DLD than in children with ALI who also committed a relatively higher proportion of Wrong Theme and No Recipient errors. It is concluded that the primary deficit of children with DLD lies in representing dependent relationships between the arguments and the verb as involved in thematic role assignment, but this is less critical in children with ALI, with their performance on the comprehension task possibly also related to other factors associated with the condition. To enhance the development of ditransitive constructions, intervention efforts for children with DLD and children with ALI could focus on strengthening the connection between each argument and its thematic role.

A Novel Prolene Suture Pull-out Technique Combined with Anchor Technique for the Treatment of Tendinous Mallet Finger.

Background: Mallet finger deformity is a prevalent disability that causes discomfort and inconvenience to the patients. Despite the existence of various surgical approaches, surgical management remains a controversial subject. Methods: We retrospectively analyzed the clinical data of 26 patients with isolated tendinous mallet fingers who were admitted between January 2021 and June 2022. Among them, there were 18 men and eight women, aged between 20 and 56 years, with an average age of 38 years. The causes of injury were cutting injuries (15 cases), sports impact injuries (nine cases), and sprains (two cases). The time interval between injury and surgery ranged from 2 hours to 48 days, with an average of 12 days. During the surgical procedure, the distal interphalangeal joint was fixed in a mild dorsiflexion position using Kirschner wire. Absorbable anchors were used to assist in the reconstruction of the insertion point of the finger extensor tendon. Additionally, a 4-0 Prolene suture was used for reinforcement. Results: All 26 patients were followed up for a period ranging from 6 to 24 months, with an average follow-up duration of 9 months. The function of distal interphalangeal joint was preserved. According to the Crawford functional evaluation criteria, the function of the affected fingers was excellent in 15 cases, good in eight cases, fair in three cases, and poor in no cases. Conclusions: A novel Prolene suture pull-out technique is an effective approach to repair tendon mallet finger and reconstruct the tendon-bone anatomical unit. This treatment option provides favorable outcomes, with high rates of excellent and good functional results.

Nonreciprocal P T-symmetric magnon laser in spinning cavity optomagnonics.

We propose a scheme to achieve nonreciprocal parity-time (P T)-symmetric magnon laser in a P T-symmetric cavity optomagnonical system. The system consists of active and passive optical spinning resonators. We demonstrate that the Fizeau light-dragging effect induced by the spinning of a resonator results in significant variations in magnon gain and stimulated emitted magnon numbers for different driving directions. We find that utilizing the Fizeau light-dragging effect allows the system to operate at ultra-low thresholds even without reaching gain-loss balance. A one-way magnon laser can also be realized across a range of parameters. High tunability of the magnon laser is achieved by changing the spinning speed of the resonators and driving direction. Our work provides a new way to explore various nonreciprocal effects in non-Hermitian magnonic systems, which may be applied to manipulate photons and magnons in multi-body non-Hermitian coupled systems.

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography.

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement. In this study, we propose a neural model based iteration algorithm which combines the graph neural network with Levenberg-Marquardt (GNNLM), which utilizes a graph data structure to represent the finite element mesh. In order to verify the performance of the graph neural network, two GNN variants, namely graph convolutional neural network (GCN) and graph attention neural network (GAT) were employed in the experiments. The results showed that GCNLM performs best in the simulation experiments within the training data distribution. However, GATLM exhibits superior performance in the simulation experiments outside the training data distribution and real experiments with breast-like phantoms. It demonstrated that the GATLM trained with simulation data can generalize well to situations outside the training data distribution without transfer training. This offers the possibility to provide more accurate absorption coefficient distributions in clinical practice.

Porous FeNi Prussian blue cubes derived carbon-based phosphides as superior sulfur hosts for high-performance lithium-sulfur batteries.

In contrast to lithium-ion batteries, lithium-sulfur batteries have higher theoretical energy density and lower cost, so they would become competitive in the practical application. However, the shuttle effect of polysulfides and slow oxidation-reduction kinetics can degrade their electrochemical performance and cycle life. In this work, we have first developed the porous FeNi Prussian blue cubes as precursors. The calcination in different atmospheres was employed to make precursors convert into common pyrolysis products or novel carbon-based phosphides, and sulfides, labeled as FeNiP/A-C, FeNiP/A-P, and FeNiP/A-S. When these products serve as host materials in the sulfur cathode, the electrochemical performance of lithium-sulfur batteries is in the order of S@FeNiP/A-P > S@FeNiP/A-S > S@FeNiP/A-C. Specifically, the initial discharge capacity of S@FeNiP/A-P can reach 679.1 mAh g-1at 1 C, and the capacity would maintain 594.6 mAh g-1after 300 cycles. That is because the combination of carbon-based porous structure and numerous well-dispersed Ni2P/Fe2P active sites contribute FeNiP/A-P to obtain larger lithium-ion diffusion, lower resistance, stronger chemisorption, and more excellent catalytic effect than other samples. This work may deliver that metal-organic framework-derived carbon-based phosphides are more suitable to serve as sulfur hosts than carbon-based sulfides or common pyrolysis products for enhancing Li-S batteries' performance.

Two-step reconstruction framework of fluorescence molecular tomography based on energy statistical probability.

Fluorescence molecular tomography (FMT), as a promising technique for early tumor detection, can non-invasively visualize the distribution of fluorescent marker probe three-dimensionally. However, FMT reconstruction is a severely ill-posed problem, which remains an obstacle to wider application of FMT. In this paper, a two-step reconstruction framework was proposed for FMT based on the energy statistical probability. First, the tissue structural information obtained from computed tomography (CT) is employed to associate the tissue optical parameters for rough solution in the global region. Then, according to the global-region reconstruction results, the probability that the target belongs to each region can be calculated. The region with the highest probability is delineated as region of interest to realize accurate and fast source reconstruction. Numerical simulations and in vivo experiments were carried out to evaluate the effectiveness of the proposed framework. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor.

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide-and-conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

Infrared Light-Emitting Diodes Based on Chirality-Sorted Carbon Nanotube Films.

An important goal in carbon nanotube optoelectronics is to achieve a high-performance near-infrared light source. But there are still many challenges such as the purity of single-walled carbon nanotube (SWCNT) chirality, nonradiative defects, thin-film quality, and device structure design. Here, we realize infrared light-emitting diodes (LEDs) based on chirality-sorted (10, 5) SWCNT network films, which operate at a low bias voltage and emit at a telecom O band of 1290 nm. Asymmetric palladium (Pd) and hafnium (Hf) contacts are used as electrodes for hole and electron injection, respectively. However, the large Schottky barrier at the interface of the SWCNTs and the Hf electrode, primarily resulting from the polymer wrapped on the nanotube surface during the sorting process, leads to inefficient electron injection and thus a low electroluminescence efficiency. We find that the efficiency of electron injection can be improved by the local doping of the nanotubes with dielectric layers of YOX-HfO2, which reduces the Schottky barrier at the SWCNT/Hf interface. Accordingly, the (10, 5) SWCNT film-based LED achieves an external quantum efficiency of larger than 0.05% without any optical coupling structure. With further improvement, we expect that such an infrared light source will have great application potential in the carbon nanotube monolithic optoelectronic integrated system and on-chip optical interconnection, especially in the field of short-distance optical fiber communications and data center.

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

Acquisition of Mandarin long passives by children with developmental language disorder.

This study investigated the comprehension and production of long passives (i.e. bei-constructions with an overt agent) in Mandarin-speaking children with developmental language disorder (DLD). Seventeen preschool children with DLD (1 female; mean age: 61 months old) and 23 typically developing (TD) children (6 females; mean age: 62 months old) participated in a sentence-picture matching task (for comprehension) and an elicited production task. Their nonverbal working memory (NVWM) was measured with the fourth edition of the Wechsler Preschool and Primary Scale of Intelligence. Results showed that children with DLD were less accurate and more likely to choose the picture with reversed thematic roles than their TD peers on passives in the sentence-picture matching task; in the elicited production task, they produced fewer target responses than TD children in passives. For NVWM, although that of the DLD group was lower than that of TD children, most children in the DLD group were within the average range. Furthermore, their performance on passives in the comprehension and production tasks was significantly correlated with their NVWM, which adds to the body of work suggesting links between complex syntax and working memory. However, the fact that NVWM could be preserved in the face of difficulties with passives suggests that this link may be due to NVWM enhancing performance during tasks with a high visual component, while it may not be underlyingly responsible for syntactic impairments in children with DLD.

Characteristics and sources of peroxyacetyl nitrate (PAN) in the rural North China Plain: Results from 1-year continuous observations.

Peroxyacetyl nitrate (PAN) is an important photochemical pollutant in the troposphere, whereas long-term measurements are scarce in rural areas in North China Plain (NCP), resulting in unclear seasonal variations and sources of PAN in rural NCP. In this study, we conducted a 1-year observation of PAN during 2021-2022 at the rural NCP site. The average concentrations of PAN were 1.10, 0.75, 0.65, and 0.88 ppbv in spring, summer, autumn, and winter, respectively, with a 1-year average of 0.81 ± 0.60 ppbv. Calculations indicate that the loss of PAN through thermal decomposition in summer accounts for 43.2% of the total formed PAN, which is an important reason for the low concentration of PAN in summer. We speculate that since the correlation between PAN and O3 in winter is significantly lower than that in other seasons, the observed regional transport of PAN cannot be ignored in winter. Through budget analysis, regional transport accounted for 12.8% and 55.9% of the observed PAN on the spring and winter pollution days, respectively, which showed that regional transport played key roles during the photochemical pollution of the rural NCP in winter. The potential source contribution function revealed that the transported PAN mainly comes from southern Hebei in spring. In winter, the transported PAN was mainly from Langfang, Hengshui, and southern Beijing. Our findings may aid in understanding PAN variations in different seasons in rural areas and highlight the impact of regional transport on the PAN budget.

Regularization parameter based on incomplete variables for X-ray luminescence computed tomography.

X-ray luminescence computed tomography (XLCT) is an emerging molecular imaging technique for biological application. However, it is still a challenge to get a stable and accurate solution of the reconstruction of XLCT. This paper presents a regularization parameter selection strategy based on incomplete variables frame for XLCT. A residual information, which is derived from Karush-Kuhn-Tucker (KKT) equivalent condition, is employed to determine the regularization parameter. This residual contains the relevant information about the solution norm and gradient norm, which improved the recovered results. Simulation and phantom experiments are designed to test the performance of the algorithm.Clinical Relevance- The results have not yet been used in clinical relevance currently, we believed that this strategy will facilitate the development of the preclinical applications in FMT.

Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects.

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT. Firstly, a parallel encoder is used to feature the original BLI & CT slices and integrate their features to distinguish the different tissue structure of imaging objects; Secondly, GRU is used to fit the spatial information of different slices and convert it into 3D features; Finally, the 3D features are decoded to the spacial and energy information of source by a symmetrical decoding structure. Our research suggested that this method can effectively compute the radiation intensity and the spatial distribution of the source for different imaging object.

Adaptive Sparsity Orthogonal Least Square with Neighbor Strategy for Fluorescence Molecular Tomography.

Fluorescence molecular tomography (FMT) is a highly sensitive and noninvasive optical imaging technique which has been widely applied to disease diagnosis and drug discovery. However, FMT reconstruction is a highly ill-posed problem. In this work, L0-norm regularization is employed to construct the mathematical model of the inverse problem of FMT. And an adaptive sparsity orthogonal least square with a neighbor strategy (ASOLS-NS) is proposed to solve this model. This algorithm can provide an adaptive sparsity and can establish the candidate sets by a novel neighbor expansion strategy for the orthogonal least square (OLS) algorithm. Numerical simulation experiments have shown that the ASOLS-NS improves the reconstruction of images, especially for the double targets reconstruction.Clinical relevance- The purpose of this work is to improve the reconstruction results of FMT. Current experiments are focused on simulation experiments, and the proposed algorithm will be applied to the clinical tumor detection in the future.