The impact of nursing interventions on the rehabilitation outcome of patients after lumbar spine surgery.

BACKGROUND: This study aimed to investigate the impact of nursing interventions on the rehabilitation outcomes of patients after lumbar spine surgery and to provide effective references for future postoperative care for patients undergoing lumbar spine surgery. METHODS: The study included two groups: a control group receiving routine care and an observation group receiving additional comprehensive nursing care. The comprehensive care encompassed postoperative rehabilitation, pain, psychological, dietary management, and discharge planning. The Visual Analogue Scale (VAS), Oswestry Disability Index (ODI), Short-Form 36 (SF-36) Health Survey, self-rating depression scale (SDS) and self-rating anxiety scale(SAS) were used to assess physiological and psychological recovery. Blood albumin, haemoglobin, neutrophil counts, white blood cell counts, red blood cell counts, inflammatory markers (IL-6, IL-10, and IFN-γ) were measured, and the incidence of postoperative adverse reactions was also recorded. RESULTS: Patients in the observation group exhibited significantly improved VAS, ODI, SF-36, SDS and SAS scores assessments post-intervention compared to the control group (P < 0.05). Moreover, levels of IL-6, IL-10, and IFN-γ were more favorable in the observation group post-intervention (P < 0.05), indicating a reduction in inflammatory response. There was no significant difference in the incidence of postoperative adverse reactions between the groups (P > 0.05), suggesting that the comprehensive nursing interventions did not increase the risk of adverse effects. CONCLUSION: Comprehensive nursing interventions have a significant impact on the postoperative recovery outcomes of patients with LSS, alleviating pain, reducing inflammation levels, and improving the overall quality of patient recovery without increasing the patient burden. Therefore, in clinical practice, it is important to focus on comprehensive nursing interventions for patients with LSS to improve their recovery outcomes and quality of life.

Critical parameters to standardize the size and concentration determination of nanomaterials by nanoparticle tracking analysis.

The size and concentration are critical for the diagnostic and therapeutic applications of nanomaterials but the accurate measurement remains challenging. Nanoparticle tracking analysis (NTA) is widely used for size and concentration determination. However, highly repeatable standard operating procedures (SOPs) are absent. We adopted the "search-evaluate-test" strategy to standardize the measurement by searching the critical parameters. The particles per frame are linearly proportional to the sample concentration and the measured results are more accurate and repeatable when the concentration is 108-109 particles/ml. The optimal detection threshold is around 5. The optimal camera level is such that it allows clear observation of particles without diffractive rings and overexposure. The optimal speed is ≤ 50 in AU and âˆ¼ 10 µl/min in flow rate. We then evaluated the protocol using polydisperse polystyrene particles and we found that NTA could discriminate particles in bimodal mixtures with high size resolution but the performance on multimodal mixtures is not as good as that of resistive pulse sensing (RPS). We further analyzed the polystyrene particles, SiO2 particles, and biological samples by NTA following the SOPs. The size and concentration measured by NTA differentially varies to those determined by RPS and transmission electron microscopy.

Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study.

BACKGROUND: Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC. METHODS: This study included NSCLC patients who underwent neck ultrasound examination followed by cervical lymph node (LN) biopsy between January 2019 and January 2022 from three institutes. Radiomic features were extracted from the ultrasound images at the maximum cross-sectional areas of cervical LNs. Logistic regression (LR) and random forest (RF) models were developed. Model performance was assessed by the area under the curve (AUC) and accuracy, validated internally and externally by fivefold cross-validation and hold-out method, respectively. RESULTS: In total, 313 patients with a median age of 64 years were included, and 276 (88.18%) had cervical LNM. Three descriptive semantic features, including long diameter, shape, and corticomedullary boundary, were selected by multivariate analysis. Out of the 474 identified radiomic features, 9 were determined to fit the LR model, while 15 fit the RF model. The average AUCs of the semantic and radiomics models were 0.876 (range: 0.781-0.961) and 0.883 (range: 0.798-0.966), respectively. However, the average AUC was higher for the semantic-radiomics combined LR model (0.901; range: 0.862-0.927). When the RF algorithm was applied, the average AUCs of the radiomics and semantic-radiomics combined models were improved to 0.908 (range: 0.837-0.966) and 0.922 (range: 0.872-0.982), respectively. The models tested by the hold-out method had similar results, with the semantic-radiomics combined RF model achieving the highest AUC value of 0.901 (95% CI, 0.886-0.968). CONCLUSIONS: The ultrasound radiomic models showed potential for accurately diagnosing cervical LNM in patients with NSCLC when integrated with descriptive semantic features. The RF model outperformed the conventional LR model in diagnosing cervical LNM in NSCLC patients.

Bacterial muropeptides promote OXPHOS and suppress mitochondrial stress in mammals.

Mitochondrial dysfunction critically contributes to many major human diseases. The impact of specific gut microbial metabolites on mitochondrial functions of animals and the underlying mechanisms remain to be uncovered. Here, we report a profound role of bacterial peptidoglycan muropeptides in promoting mitochondrial functions in multiple mammalian models. Muropeptide addition to human intestinal epithelial cells (IECs) leads to increased oxidative respiration and ATP production and decreased oxidative stress. Strikingly, muropeptide treatment recovers mitochondrial structure and functions and inhibits several pathological phenotypes of fibroblast cells derived from patients with mitochondrial disease. In mice, muropeptides accumulate in mitochondria of IECs and promote small intestinal homeostasis and nutrient absorption by modulating energy metabolism. Muropeptides directly bind to ATP synthase, stabilize the complex, and promote its enzymatic activity in vitro, supporting the hypothesis that muropeptides promote mitochondria homeostasis at least in part by acting as ATP synthase agonists. This study reveals a potential treatment for human mitochondrial diseases.

Allophane improves anaerobic digestion of chicken manure by alleviating ammonia inhibition and intensifying direct interspecies electron transfer.

Synthesized allophane was employed in anaerobic digestion of chicken manure to improve the stability and methane production under ammonia inhibition. Adding 0.5 %, 1.0 % and 1.5 % (w/w) allophane increased the methane production by 261 âˆ¼ 350 % compared with the group without allophane addition. Further investigation indicated that the maximum adsorption capacity of allophane for NH4+-N achieved at 261.9 mg/g; it suggested that allophane adsorption potentially alleviated the ammonia inhibition, which also was reflected by the increase in the activity of the related enzyme, such as coenzyme F420. Moreover, allophane addition also intensified the direct interspecies electron transfer (DIET) in anaerobic digestion; it can be well supported by the increased relative abundance of Methanosaeta and Methanosarcina involved in the DIET. Overall, the improved anaerobic digestion via alleviating ammonia inhibition and intensifying DIET by allophane was elucidated comprehensively, which can contribute to the development of a functional additive for efficient anaerobic digestion in practical application.

Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.

BACKGROUND: Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC. METHODS: This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020. The radiomic features of the tumor and SLNs were extracted from preoperative CT, respectively. Ninety machine learning (ML) models were developed based on tumor region, SLNs region, and tumor-SLNs dual-region. The model performance was assessed by the area under the curve (AUC) and validated internally by fivefold cross-validation. RESULTS: In total, 202 patients were included in this study. ML models based on dual-region radiomics showed good performance for SLNM prediction, with a median AUC of 0.794 (range, 0.686-0.880), which was superior to those of models based on tumor region (median AUC, 0.746; range, 0.630-0.811) and SLNs region (median AUC, 0.700; range, 0.610-0.842). The ML model, which is developed by using the naive Bayes algorithm and dual-region features, had the highest AUC of 0.880 (range of cross-validation, 0.825-0.937) among all ML models. The optimal logistic regression model was inferior to the optimal ML model for predicting SLNM, with an AUC of 0.727. CONCLUSIONS: The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.

Increased resected lymph node stations improved survival of esophageal squamous cell carcinoma.

BACKGROUND: Neoadjuvant chemoradiotherapy (nCRT) and surgery have been recommended as the standard treatments for locally advanced esophageal squamous cell carcinoma (ESCC). In addition, nodal metastases decreased in frequency and changed in distribution after neoadjuvant therapy. This study aimed to examine the optimal strategy for lymph node dissection (LND) in patients with ESCC who underwent nCRT. METHODS: The hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS) were calculated using the Cox proportional hazard model. To determine the minimal number of LNDs (n-LNS) or least station of LNDs (e-LNS), the Chow test was used. RESULTS: In total, 333 patients were included. The estimated cut-off values for e-LNS and n-LNS were 9 and 15, respectively. A higher number of e-LNS was significantly associated with improved OS (HR: 0.90; 95% CI 0.84-0.97, P = 0.0075) and DFS (HR: 0.012; 95% CI: 0.84-0.98, P = 0.0074). The e-LNS was a significant prognostic factor in multivariate analyses. The local recurrence rate of 23.1% in high e-LNS is much lower than the results of low e-LNS (13.3%). Comparable morbidity was found in both the e-LNS and n-LND subgroups. CONCLUSION: This cohort study revealed an association between the extent of LND and overall survival, suggesting the therapeutic value of extended lymphadenectomy during esophagectomy. Therefore, more lymph node stations being sampled leads to higher survival rates among patients who receive nCRT, and standard lymphadenectomy of at least 9 stations is strongly recommended.

Pulmonary blastoma is successfully treated with immunotherapy and targeted therapy.

Pulmonary blastomas (PB) are an extremely rare type of lung cancer. Currently, no standard treatment exists for PB. Immunotherapy with checkpoint inhibitors and anti-angiogenesis treatments has been an effective method for lung cancer; however, studies on PB treatment are lacking. Herein, we present a case report of successful conversion therapy with immunotherapy and targeted therapy for PB. After receiving treatment with a PD-1 inhibitor (penpulimab) and a multi-target tyrosine kinase inhibitor (anlotinib) treatment, the patient showed an impressive response and underwent a successful operation. We also summarized and reviewed literature reports on PubMed from January 1, 2000, to December 31, 2022, using the keyword "pulmonary blastoma", discussing the efficacy and specifics of chemotherapy and radiotherapy. Immunotherapy, in combination with targeted therapy, should be considered a potential therapeutic strategy for PB.

Perovskites as oxygen storage materials for chemical looping partial oxidation and reforming of methane.

The traditional partial oxidation, dry reforming and steam reforming of methane technologies are separated into two reactors for execution by chemical looping technology, which can avoid the defects exposed in the traditional process (avoiding carbon accumulation, reducing costs, etc.). The key to chemical looping technology is to find suitable oxygen carriers (OCs), which can store and release oxygen to form a closed loop in the chemical looping. The purpose of this review is to summarize the current status of perovskite oxides for partial oxidation and reforming of methane in chemical looping, describe the structure, oxygen capacity, oxygen migration rate and common synthesis methods of perovskites in chemical looping. In addition, the effects of impregnation loading, ion doping, and structural morphology on the catalytic conversion of CH4 by perovskite OCs and the reaction mechanism on the OCs are also discussed.

How does adsorptive fractionation of dissolved black carbon on ferrihydrite affect its copper binding behaviors? A molecular-scale investigation.

Adsorptive fractionation of dissolved black carbon (DBC) on minerals is proven to alter its molecular composition, which will inevitably affect the environment fate of heavy metals. However, the effects of molecular fractionation on the interaction between DBC and heavy metals remain unclear. Herein, we observed that the selective adsorption of ferrihydrite caused molecular changes of DBC from high molecular weight/unsaturation/aromaticity to low molecular weight/saturation/aliphatics. This process accompanied by a retention of carbohydrate and a reduction of oxygen-rich functional groups (e.g., polyphenols and carboxyl) and long carbon chain in DBC. The residual DBC in aqueous phase demonstrated a weaker binding affinity to copper compared to the original DBC. This decrease in binding affinity was primarily attributed to the adsorption of polycyclic condensed aromatic compounds of 200-250 Da, oxygen-rich polycyclic condensed aromatic compounds of 250-300 Da, oxygen-rich non-polycyclic aromatic compounds of 300-450 Da, and non-polycyclic aromatic compounds of 450-700 Da in DBC by ferrihydrite. Additionally, the retention of carbohydrates and aliphatic compounds of 300-450 Da also made a significant contribution. Notably, carboxylic groups rather than phenolic groups were the dominant oxygen-containing functional groups responsible for this affinity reduction. This study has significant implications for understanding of the biogeochemical processes of DBC at soil-water interface and surface water, especially its role in the transportation of heavy metals.

Integrating single-cell and spatial transcriptomics reveals endoplasmic reticulum stress-related CAF subpopulations associated with chordoma progression.

BACKGROUND: With cancer-associated fibroblasts (CAFs) as the main cell type, the rich myxoid stromal components in chordoma tissues may likely contribute to its development and progression. METHODS: Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, bulk RNA-seq, and multiplexed quantitative immunofluorescence (QIF) were used to dissect the heterogeneity, spatial distribution, and clinical implication of CAFs in chordoma. RESULTS: We sequenced here 72 097 single cells from 3 primary and 3 recurrent tumor samples, as well as 3 nucleus pulposus samples as controls using scRNA-seq. We identified a unique cluster of CAF in recurrent tumors that highly expressed hypoxic genes and was functionally enriched in endoplasmic reticulum stress (ERS). Pseudotime trajectory and cell communication analyses showed that this ERS-CAF subpopulation originated from normal fibroblasts and widely interacted with tumoral and immune cells. Analyzing the bulk RNA-seq data from 126 patients, we found that the ERS-CAF signature score was associated with the invasion and poor prognosis of chordoma. By integrating the results of scRNA-seq with spatial transcriptomics, we demonstrated the existence of ERS-CAF in chordoma tissues and revealed that this CAF subtype displayed the most proximity to its surrounding tumor cells. In subsequent QIF validation involving 105 additional patients, we confirmed that ERS-CAF was abundant in the chordoma microenvironment and located close to tumor cells. Furthermore, both ERS-CAF density and its distance to tumor cells were correlated with tumor malignant phenotype and adverse patient outcomes. CONCLUSIONS: These findings depict the CAF landscape for chordoma and may provide insights into the development of novel treatment approaches.

Lewis acid/base mediated deep eutectic solvents intensify lignocellulose fractionation to facilitate enzymatic hydrolysis and lignin nanosphere preparation.

In this work, Lewis acids (FeCl3, AlCl3) and bases (Na2CO3, Na2SO3) were incorporated into a neutral deep eutectic solvent (DES, choline chloride/glycerin) to intensify the lignocellulose fractionation. The efficiency of fractionation in terms of the maximum delignification rate (89.7 %) and well-pleasing cellulose saccharification (100 %) could be achieved by the Lewis acid-mediated DES. An in-depth insight of the evolution of lignin structure revealed that Lewis acid-mediated DES could cleave the ß-O-4 linkages efficiently to achieve a high yield lignin fragments. Meanwhile, the lignin fragments with the enhanced amphiphilic properties facilitate the preparation of lignin nanospheres (LNSs) via the self-assembly process. The resultant LNSs extracted by Lewis acid-mediated DES exhibited an excellent thermal stability, and enhanced antibacterial capacity, which were associated with the phenolic OH content. However, the extracted lignin by Lewis base-mediated DES was mainly attributed to the cleavage of lignin-carbohydrate complexes bond, especially the lignin-carbohydrate ester bond, which retained more ether bonds and a relatively complete structure. This study illuminated the different mechanisms of lignin extraction and the structural evolution of lignin from Lewis acid/base-mediated DES, and provided guidance to select suitable fractionation techniques for upgrading the downstream products.

Two all-biomass cellulose/amino acid spherical nanoadsorbents based on a tri-aldehyde spherical nanocellulose II amino acid premodification platform for the efficient removal of Cr(VI) and Cu(II).

Adsorbents consisting of spherical nanoparticles exhibit superior adsorption performance and hence, have immense potential for various applications. In this study, a tri-aldehyde spherical nanoadsorbent premodification platform (CTNAP), which can be grafted with various amino acids, was synthesized from corn stalk. Subsequently, two all-biomass spherical nanoadsorbents, namely, cellulose/l-lysine (CTNAP-Lys) and cellulose/L-cysteine (CTNAP-Cys), were prepared. The morphologies as well as chemical and crystal structures of the two adsorbents were studied in detail. Notably, the synthesized adsorbents exhibited two important characteristics, namely, a spherical nanoparticle morphology and cellulose II crystal structure, which significantly enhanced their adsorption performance. The mechanism of the adsorption of Cr(VI) onto CTNAP-Lys and that of Cu(II) onto CTNAP-Cys were studied in detail, and the adsorption capacities were determined to be as high as 361.69 (Cr(VI)) and 252.38 mg/g (Cu(II)). Using the proposed strategy, it should be possible to prepare other all-biomass cellulose/amino acid spherical nanomaterials with high functional group density for adsorption, medical, catalytic, analytical chemistry, corrosion, and photochromic applications.

Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions.

Barrett's esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the "black box" nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.

Construction and validation of a predictive model for postoperative urinary retention after lumbar interbody fusion surgery.

BACKGROUND: Postoperative urine retention (POUR) after lumbar interbody fusion surgery may lead to recatheterization and prolonged hospitalization. In this study, a predictive model was constructed and validated. The objective was to provide a nomogram for estimating the risk of POUR and then reducing the incidence. METHODS: A total of 423 cases of lumbar fusion surgery were included; 65 of these cases developed POUR, an incidence of 15.4%. The dataset is divided into a training set and a validation set according to time. 18 candidate variables were selected. The candidate variables were screened through LASSO regression. The stepwise regression and random forest analysis were then conducted to construct the predictive model and draw a nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve were used to evaluate the predictive effect of the model. RESULTS: The best lambda value in LASSO was 0.025082; according to this, five significant variables were screened, including age, smoking history, surgical method, operative time, and visual analog scale (VAS) score of postoperative low back pain. A predictive model containing four variables was constructed by stepwise regression. The variables included age (ß = 0.047, OR = 1.048), smoking history (ß = 1.950, OR = 7.031), operative time (ß = 0.022, OR = 1.022), and postoperative VAS score of low back pain (ß = 2.554, OR = 12.858). A nomogram was drawn based on the results. The AUC of the ROC curve of the training set was 0.891, the validation set was 0.854 in the stepwise regression model. The calibration curves of the training set and validation set are in good agreement with the actual curves, showing that the stepwise regression model has good prediction ability. The AUC of the training set was 0.996, and that of the verification set was 0.856 in the random forest model. CONCLUSION: This study developed and internally validated a new nomogram and a random forest model for predicting the risk of POUR after lumbar interbody fusion surgery. Both of the nomogram and the random forest model have high accuracy in this study.

Vibration-enhanced disposable electroanalytical platform for selective analysis of tryptophan in fruits based on molecular imprinting.

Although electrochemical detection based on molecular imprinting polymers (MIP) could dramatically improve the selectivity, the procedure is time-consuming because of the essential incubation step. In addition, current MIP electrochemical detections were not suitable for analysis of microliter-level sample solutions, limiting their applications for real samples. This investigation aims at applying vibration to enhance efficiency of MIP electrochemical detection of 20 µL sample solutions. MIP analysis of Tryptophan (Trp) was used as the model with disposable MIP electrodes prepared by electrochemical polymerization of o-phenylenediamine on carbon ink coated on stainless steel sheets. The MIP electrode was integrated in a 3D-printed analytical device for vibration-enhanced electrochemical detection of Trp. Our results showed that this vibration-enhanced strategy could significantly increase electrochemical responses of Trp at the same incubation time. Such improvement might be attributed to the enhanced mass transfer at the surface of the working electrode brought by vibration. It needs to be emphasized that this strategy is suitable for analysis of sample solutions with the volume of microliters, which is superior to normal stirring in MIP electrochemical detection. Our approach could be successfully utilized for differentiation of Trp in different fruits, opening more opportunities for MIP electrochemical detection of real samples. The enhanced efficiency by vibration could pave foundation for extensive practical MIP detection of sample solutions at the level of microliters.