Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression.

Introduction: Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC. METHODS: A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA). RESULTS: The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds. CONCLUSION: The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.

iRGD-Guided Silica/Gold Nanoparticles for Efficient Tumor-Targeting and Enhancing Antitumor Efficacy Against Breast Cancer.

Background: Breast cancer presents significant challenges due to the limited effectiveness of available treatments and the high likelihood of recurrence. iRGD possesses both RGD sequence and C-terminal sequence and has dual functions of targeting and membrane penetration. iRGD-modified nanocarriers can enhance drug targeting of tumor vascular endothelial cells and penetration of new microvessels, increasing drug concentration in tumor tissues. Methods: The amidation reaction was carried out between SiO2/AuNCs and iRGD/PTX, yielding a conjugated drug delivery system (SiO2/AuNCs-iRGD/PTX, SAIP@NPs). The assessment encompassed the characterization of the morphology, particle size distribution, physicochemical properties, in vitro release profile, cytotoxicity, and cellular uptake of SAIP@NPs. The tumor targeting and anti-tumor efficacy of SAIP@NPs were assessed using a small animal in vivo imaging system and a tumor-bearing nude mice model, respectively. The tumor targeting and anti-tumor efficacy of SAIP@NPs were assessed utilizing a small animal in vivo imaging system and an in situ nude mice breast cancer xenograft model, respectively. Results: The prepared SAIP@NPs exhibited decent stability and a certain slow-release effect in phosphate buffer (PBS, pH 7.4). In vitro studies had shown that, due to the dual functions of transmembrane and targeting of iRGD peptide, SAIP@NPs exhibited strong binding to integrin αvß3, which was highly expressed on the membrane of MDA-MB-231 cells, improving the uptake capacity of tumor cells, inhibiting the rapid growth of tumor cells, and promoting tumor cell apoptosis. The results of animal experiments further proved that SAIP@NPs had longer residence time in tumor sites, stronger anti-tumor effect, and no obvious toxicity to major organs of experimental animals. Conclusion: The engineered SAIP@NPs exhibited superior functionalities including efficient membrane permeability, precise tumor targeting, and imaging, thereby significantly augmenting the therapeutic efficacy against breast cancer with a favorable safety profile.

Combined effects of nitrogen limitation and overexpression of malic enzyme gene on lipid accumulation in Dunaliella parva.

Overexpression of Dunaliella parva (D. parva) malic enzyme (ME) gene (DpME) significantly increased DpME expression and ME enzyme activity in transgenic D. parva. Nitrogen limitation had an inhibitory effect on protein content, and DpME overexpression could improve protein content. Nitrogen limitation increased carbohydrate content, and Dunaliella parva overexpressing malic enzyme gene under nitrogen limitation (DpME-N-) group showed the lowest starch content among all groups. Dunaliella parva overexpressing malic enzyme gene under nitrogen sufficient condition (DpME) and DpME-N- groups showed considerably high mRNA levels of DpME. ME activity was significantly enhanced by DpME overexpression, and nitrogen limitation caused a smaller increase. DpME overexpression and nitrogen limitation obviously enhanced lipid accumulation, and DpME overexpression had more obvious effect. Compared with control (wild type), lipid content (68.97%) obviously increased in DpME-N- group. This study indicated that the combination of DpME overexpression and nitrogen limitation was favorable to the production of microalgae biodiesel.

Renal clear cell carcinoma undergoing cystic change: A cases report and review of the literature.

INTRODUCTION: We presented a case diagnosed the renal clear cell carcinoma undergoing cystic change (RCCCC) with detailed clinical data. Along with literature review, we aimed to investigate clinical diagnosis and treatment of RCCCC and explore the differential diagnosis of RCCCC and multilocular cystic renal cell carcinoma (MCRCC). CASE PRESENTATION: The patient was diagnosed with a right renal cyst after physical examination, which was misdiagnosed as a renal cyst by imaging examination. Intraoperative surgical treatment was performed to remove the roof and decompress the renal cyst. Rapid pathology revealed MCRCC with low malignant potential during laparoscopic right renal cyst decompression. Radical nephrectomy was performed with the family's signature. The postoperative pathological diagnosis was clear cell carcinoma cystic lesion of kidney (RCCCC). No recurrence or metastasis during 1 year follow-up. CLINICAL DISCUSSION: RCCCC cases were similar to classical clear cell renal carcinoma. Radical nephrectomy should be avoided in patients with MCRCC, and radical nephrectomy should be chosen in patients with RCCCC, with postoperative and close follow-up. Unroofing decompression of renal cyst was performed during the operation, and the risk of tumor implantation and metastasis was worried after the operation. The patient agreed to receive eight cycles of immune checkpoint inhibitor therapy after surgery. Adrenal insufficiency occurred after 8 cycles of immune checkpoint inhibitor therapy(ICIs), then the immunotherapy was discontinued. CONCLUSION: RCCCC is a rare and special type of renal clear cell carcinoma, and its prognosis is the same as that of renal clear cell carcinoma. The preoperative diagnosis of RCCCC mainly depends on imaging examination (CT or B-ultrasound). The early differential diagnosis from multilocular cystic renal cell carcinoma is difficult, and the diagnosis usually depends on postoperative pathological diagnosis.

Rheumatoid arthritis complicated with cervical actinomycosis and ureteral obstruction: A case report and literature review. / ç±»é£Žæ¹¿å ³èŠ‚ç‚Žåˆå¹¶å®«é¢ˆæ”¾çº¿èŒç— åŠè¾“å°¿ç®¡æ¢—é˜»1ä¾‹å¹¶æ–‡çŒ®å¤ä¹ .

Actinomycosis is a rare chronic granulomatous disease characterized by granuloma formation and tissue fibrosis with sinus tracts, often misdiagnosed due to its similarity to many infectious and non-infectious diseases. This report presents a case of a 60-year-old female with more than 10 years history of rheumatoid arthritis who developed actinomycosis infection after long-term treatment with immunosuppressants and biologics, including methotrexate, leflunomide, and infliximab, leading to recurrent joint pain, poorly controlled rheumatoid arthritis activity, and persistent elevation of white blood cell counts. Abdominal CT revealed a pelvic mass and right ureteral dilation. Pathological examination of cervical tissue showed significant neutrophil infiltration and sulfur granules, indicating actinomycosis. The patient received 18 months of doxycycline treatment for the infection and continued rheumatoid arthritis therapy with leflunomide, hydroxychloroquine sulfate, and tofacitinib, resulting in improved joint symptoms and normalized white blood cell counts. After 2 years of follow-up, the patient remained stable with no recurrence. This case highlights the importance of clinicians being vigilant for infections, particularly chronic, occult infections from rare pathogens, in rheumatoid arthritis patients on potent immunosuppressants and biologics, advocating for early screening and diagnosis.

TME-NET: an interpretable deep neural network for predicting pan-cancer immune checkpoint inhibitor responses.

Immunotherapy with immune checkpoint inhibitors (ICIs) is increasingly used to treat various tumor types. Determining patient responses to ICIs presents a significant clinical challenge. Although components of the tumor microenvironment (TME) are used to predict patient outcomes, comprehensive assessments of the TME are frequently overlooked. Using a top-down approach, the TME was divided into five layers-outcome, immune role, cell, cellular component, and gene. Using this structure, a neural network called TME-NET was developed to predict responses to ICIs. Model parameter weights and cell ablation studies were used to investigate the influence of TME components. The model was developed and evaluated using a pan-cancer cohort of 948 patients across four cancer types, with Area Under the Curve (AUC) and accuracy as performance metrics. Results show that TME-NET surpasses established models such as support vector machine and k-nearest neighbors in AUC and accuracy. Visualization of model parameter weights showed that at the cellular layer, Th1 cells enhance immune responses, whereas myeloid-derived suppressor cells and M2 macrophages show strong immunosuppressive effects. Cell ablation studies further confirmed the impact of these cells. At the gene layer, the transcription factors STAT4 in Th1 cells and IRF4 in M2 macrophages significantly affect TME dynamics. Additionally, the cytokine-encoding genes IFNG from Th1 cells and ARG1 from M2 macrophages are crucial for modulating immune responses within the TME. Survival data from immunotherapy cohorts confirmed the prognostic ability of these markers, with p-values <0.01. In summary, TME-NET performs well in predicting immunotherapy responses and offers interpretable insights into the immunotherapy process. It can be customized at https://immbal.shinyapps.io/TME-NET.

Corticomuscular Coupling Analysis in Stroke Rehabilitation Based on Variational Mode Decomposition-Transfer Entropy.

OBJECTIVE: This study aims to explore alterations in corticomuscular and cortical coupling during the rehabilitation of stroke patients. METHODS: We initiated the analysis by employing variational modal decomposition (VMD) on electromyography (EMG) data, followed by the application of VDM-transfer entropy (VMD-TE) to quantify the coupling strength between electroencephalogram (EEG) and EMG signals. Subsequently, we constructed the VMD-TE connection matrix and analyzed the clustering coefficient and small-world attributes within the cortico-muscular functional network (CMFN). Finally, a random forest algorithm was employed to extract features from the VMD-TE connection matrix across different rehabilitation periods. RESULTS: Beta waves in EEG were emerged as the key information carrier between the cortex and muscle, and the CMFN of patients with the beta frequency band has small-world characteristics. During rehabilitation, we observed a decrease in coupling between the initially affected motor cortex and muscle, accompanied by an increase in coupling between the frontal region and muscle. CONCLUSION: Our findings suggest potential neuro-remodeling in stroke patients after rehabilitation, with CFMN serving as a valuable metric for assessing cortico-muscular coupling.

Trilogy of comprehensive treatment of kitchen waste by bacteria-microalgae-fungi combined system: Pretreatment, water purification and resource utilization, and biomass harvesting.

Given its profound disservice, a bacteria-microalgae-fungi combined system was designed to treat kitchen waste. Firstly, a new type of microbial agent homemade compound microorganisms (HCM) (composed of Serratia marcescens, Bacillus subtilis and other 11 strains) with relatively high bio-security were developed for pretreating kitchen waste, and HCM efficiently degraded 85.2 % cellulose, 94.3 % starch, and 59.0 % oil. HCM also accomplished brilliantly the initial nutrients purification and liquefaction conversion of kitchen waste. Under mono-culture mode (fungi and microalgae were inoculated separately in the pre - and post-stages) and co-culture mode (fungi and microalgae were inoculated simultaneously in the early stage), microalgae-fungi consortia were then applied for further water purification and resource utilization of kitchen waste liquefied liquid (KWLL) produced in the pretreatment stage. Two kinds of microalgae-fungi consortia (Chlorella sp. HQ and Chlorella sp. MHQ2 form consortia with pellet-forming fungi Aspergillus niger HW8-1, respectively) removed 79.5-83.0 % chemical oxygen demand (COD), 44.0-56.5 % total nitrogen (TN), 90.3-96.4 % total phosphorus (TP), and 64.9-71.0 % NH4+-N of KWLL. What's more, the microalgae-fungi consortia constructed in this study accumulated abundant high-value substances at the same time of efficiently purifying KWLL. Finally, in the biomass harvesting stage, pellet-forming fungi efficiently harvested 81.9-82.1 % of microalgal biomass in a low-cost manner through exopolysaccharides adhesion, surface proteins interaction and charge neutralization. Compared with conventional microalgae-bacteria symbiosis system, the constructed bacteria-microalgae-fungi new-type combined system achieves the triple purpose of efficient purification, resource utilization, and biomass recovery on raw kitchen waste through the trilogy strategy, providing momentous technical references and more treatment systems selection for future kitchen waste treatment.

Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study.

BACKGROUND: Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis. METHODS: This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns. RESULTS: The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability. CONCLUSIONS: Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

Cardiac autonomic regulation following a 246-km mountain ultra-marathon: An observational study.

Physical exercise requires integrated autonomic and cardiovascular adjustments to maintain homeostasis. We aimed to observe acute posture-related changes in blood pressure, and apply a portable noninvasive monitor to measure the heart index for detecting arrhythmia among elite participants of a 246-km mountain ultra-marathon. Nine experienced ultra-marathoners (8 males and 1 female) participating in the Run Across Taiwan Ultra-marathon in 2018 were enrolled. The runners' Heart Spectrum Blood Pressure Monitor measurements were obtained in the standing and supine positions before and immediately after the race. Their high-sensitivity troponin T and N-terminal proB-type natriuretic peptide levels were analyzed 1 week before and immediately after the event. Heart rate was differed significantly in the immediate postrace assessment compared to the prerace assessment, in both the standing (P = .011; d = 1.19) and supine positions (P = .008; d = 1.35). Postural hypotension occurred in 4 (44.4%) individuals immediately postrace. In 3 out of 9 (33.3%) recruited finishers, the occurrence of premature ventricular complex signals in the standing position was detected; premature ventricular complex signal effect was observed in the supine position postrace in only 1 participant (11.1%). Premature ventricular complex signal was positively correlated with running speed (P = .037). Of the 6 individuals who completed the biochemical tests postrace, 2 (33.3%) had high-sensitivity troponin T and 6 (100%) had N-terminal proB-type natriuretic peptide values above the reference interval. A statistically significant increase was observed in both the high-sensitivity troponin T (P = .028; d = 1.97), and N-terminal proB-type natriuretic peptide (P = .028; d = 2.91) levels postrace compared to prerace. In conclusion, significant alterations in blood pressure and heart rate were observed in the standing position, and postexercise (postural) hypotension occurred among ultra-marathoners. The incidence of premature ventricular complexes was higher after the race than before.

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

Medicaid expansion in California and breast cancer incidence across neighborhoods with varying social vulnerabilities.

PURPOSE: To investigate changes in breast cancer incidence rates associated with Medicaid expansion in California. METHODS: We extracted yearly census tract-level population counts and cases of breast cancer diagnosed among women aged between 20 and 64 years in California during years 2010-2017. Census tracts were classified into low, medium and high groups according to their social vulnerability index (SVI). Using a difference-in-difference (DID) approach with Poisson regression models, we estimated the incidence rate, incidence rate ratio (IRR) during the pre- (2010-2013) and post-expansion periods (2014-2017), and the relative IRR (DID estimates) across three groups of neighborhoods. RESULTS: Prior to the Medicaid expansion, the overall incidence rate was 93.61, 122.03, and 151.12 cases per 100,000 persons among tracts with high, medium, and low-SVI, respectively; and was 96.49, 122.07, and 151.66 cases per 100,000 persons during the post-expansion period, respectively. The IRR between high and low vulnerability neighborhoods was 0.62 and 0.64 in the pre- and post-expansion period, respectively, and the relative IRR was 1.03 (95% CI 1.00 to 1.06, p = 0.026). In addition, significant DID estimate was only found for localized breast cancer (relative IRR = 1.05; 95% CI, 1.01 to 1.09, p = 0.049) between high and low-SVI neighborhoods, not for regional and distant cancer stage. CONCLUSIONS: The Medicaid expansion had differential impact on breast cancer incidence across neighborhoods in California, with the most pronounced increase found for localized cancer stage in high-SVI neighborhoods. Significant pre-post change was only found for localized breast cancer between high and low-SVI neighborhoods.

Heteroaggregation kinetics of oppositely charged nanoplastics in aquatic environments: Effects of particle ratio, solution chemistry, and interaction sequence.

Interactions between positively charged amino-modified (APS) and negatively charged bare (BPS) polystyrene nanoplastics may cause heteroaggregation in aquatic environments. This study investigated the effects of particle concentration ratio, solution chemistry [electrolytes, pH, and natural organic matter (NOM)], and interaction sequence on their heteroaggregation kinetics. In the absence of electrolytes and NOM, the APS/BPS ratio for attaining maximum heteroaggregation rate (khetero) increased from APS/BPS= 3/7 to APS/BPS= 1/1 as pH increased from 4 to 10, indicating that electrostatic interactions dominated heteroaggregation. In the absence of NOM, khetero ranked APS/BPS= 2/3 > APS/BPS= 1/1 > APS/BPS= 3/2. Colloidal stability decreased linearly as pH increased from 4 to 8 at APS/BPS= 1/1, while diffusion-limited heteroaggregation persisted at pH 10. In NaCl solution, humic acid (HA) retarded heteroaggregation more effectively than sodium alginate (SA) via steric hindrance and weakening electrostatic interactions, following the modified Derjaguin-Landau-Verwey-Overbeek (MDLVO) theory. Compared with simultaneous interactions among APS, BPS, NaCl, and NOM, the NOM retardation effects on heteroaggregation weakened if delaying its interaction with others. In CaCl2 solution, the effects of NOM on heteroaggregation depended on counterbalance among charge screening, steric hindrance, and calcium bridging. These findings highlight the important role of heteroaggregation between oppositely charged nanoplastics on their fate and transport in aquatic environments.

Vascular calcification: High incidence sites, distribution, and detection.

Vascular calcification is an important pathological change in a variety of disease states such as atherosclerosis (AS), diabetes, chronic kidney disease (CKD), hypertension, and is a strong predictor of cardiovascular events. The distribution and location of calcification in different vessels may have different clinical effects and prognosis. Therefore, the study of high-risk sites of vascular calcification will help us to better understand the prevention, diagnosis, and treatment of related diseases, as well as to evaluate the efficacy and prognosis. So far, although there are some studies on the sites with high incidence of vascular calcification, there is a lack of systematic sorting out the distribution and location of vascular calcification in humans. Based on this, relevant databases were searched, literatures were retrieved, analyzed, and summarized, and the locations of high incidence of vascular calcification and their distribution characteristics, the relationship between high incidence of vascular calcification and hemodynamics, and the common detection methods of high incidence of vascular calcification were systematically described, hoping to provide help for clinical and research.

Activating transcription factor 6 contributes to cisplatin­induced ototoxicity via regulating the unfolded proteins response.

As a broad-spectrum anticancer drug, cisplatin is widely used in the treatment of tumors in various systems. Unfortunately, several serious side effects of cisplatin limit its clinical application, the most common of which are nephrotoxicity and ototoxicity. Studies have shown that cochlear hair cell degeneration is the main cause of cisplatin-induced hearing loss. However, the mechanism of cisplatin-induced hair cell death remains unclear. The present study aimed to explore the potential role of activating transcription factor 6 (ATF6), an endoplasmic reticulum (ER)-localized protein, on cisplatin-induced ototoxicity in vivo and in vitro. In this study, we observed that cisplatin exposure induced apoptosis of mouse auditory OC-1 cells, accompanied by a significant increase in the expression of ATF6 and C/EBP homologous protein (CHOP). In cell or cochlear culture models, treatment with an ATF6 agonist, an ER homeostasis regulator, significantly ameliorated cisplatin-induced cytotoxicity. Further, our in vivo experiments showed that subcutaneous injection of an ATF6 agonist almost completely prevented outer hair cell loss and significantly alleviated cisplatin-induced auditory brainstem response (ABR) threshold elevation in mice. Collectively, our results revealed the underlying mechanism by which activation of ATF6 significantly improved cisplatin-induced hair cell apoptosis, at least in part by inhibiting apoptosis signal-regulating kinase 1 expression, and demonstrated that pharmacological activation of ATF6-mediated unfolded protein response is a potential treatment for cisplatin-induced ototoxicity.

Modeling and Experiments on Temperature and Electrical Conductivity Characteristics in High-Temperature Heating of Carbide-Bonded Graphene Coating on Silicon.

A novel non-isothermal glass hot embossing system utilizes a silicon mold core coated with a three-dimensional carbide-bonded graphene (CBG) coating, which acts as a thin-film resistance heater. The temperature of the system significantly influences the electrical conductivity properties of silicon with a CBG coating. Through simulations and experiments, it has been established that the electrical conductivity of silicon with a CBG coating gradually increases at lower temperatures and rapidly rises as the temperature further increases. The CBG coating predominantly affects electrical conductivity until 400 °C, after which silicon becomes the dominant factor. Furthermore, the dimensions of CBG-coated silicon and the reduction of CBG coating also affect the rate and outcome of conductivity changes. These findings provide valuable insights for detecting CBG-coated silicon during the embossing process, improving efficiency, and predicting the mold core's service life, thus enhancing the accuracy of optical lens production.

Exosome-loaded hyaluronic acid hydrogel composite with oxygen-producing 3D printed polylactic acid scaffolds for bone tissue repair and regeneration.

Bone defects can interfere with bone healing by disrupting the local environment, resulting in vascular damage and hypoxia. Under these conditions, insufficient oxygen availability is a significant factor that exacerbates disease by blocking angiogenesis or osteogenesis. Exosomes play a crucial role in intercellular communication and modulation of inflammation to aid bone regeneration. However, the distance between exosomes and areas of damage can hinder efficient bone generation and cell survival. To overcome this limitation, we fabricated a continuous oxygen-supplying composite scaffold, with the encapsulation of calcium peroxide in a polylactic acid three-dimensional (3D) printing construct (CPS), as both an oxygen source and hydroxyapatite (HAP) precursor. Furthermore, bone marrow mesenchymal stem cell (BMSC)-derived exosomes were incorporated into hyaluronic acid (HA) hydrogels to stimulate cell growth and modulate inflammation. The release of exosomes into cells leads to an increase in alkaline phosphatase production. In vivo results demonstrated that the composite scaffold regulated the inflammatory microenvironment, relieved tissue hypoxia, and promoted new bone formation. These results indicate that the synergistic effect of exosomes and oxygen promoted the proliferation of BMSCs, alleviated inflammation and exhibited excellent osteogenic properties. In conclusion, this osteogenic functional composite scaffold material offers a highly effective approach for bone repair.

DCDiff: Dual-Granularity Cooperative Diffusion Models for Pathology Image Analysis.

Whole Slide Images (WSIs) are paramount in the medical field, with extensive applications in disease diagnosis and treatment. Recently, many deep-learning methods have been used to classify WSIs. However, these methods are inadequate for accurately analyzing WSIs as they treat regions in WSIs as isolated entities and ignore contextual information. To address this challenge, we propose a novel Dual-Granularity Cooperative Diffusion Model (DCDiff) for the precise classification of WSIs. Specifically, we first design a cooperative forward and reverse diffusion strategy, utilizing fine-granularity and coarse-granularity to regulate each diffusion step and gradually improve context awareness. To exchange information between granularities, we propose a coupled U-Net for dual-granularity denoising, which efficiently integrates dual-granularity consistency information using the designed Fine- and Coarse-granularity Cooperative Aware (FCCA) model. Ultimately, the cooperative diffusion features extracted by DCDiff can achieve cross-sample perception from the reconstructed distribution of training samples. Experiments on three public WSI datasets show that the proposed method can achieve superior performance over state-of-the-art methods. The code is available at https://github.com/hemo0826/DCDiff.

Recognition of regions of stroke injury using multi-modal frequency features of electroencephalogram.

Objective: Nowadays, increasingly studies are attempting to analyze strokes in advance. The identification of brain damage areas is essential for stroke rehabilitation. Approach: We proposed Electroencephalogram (EEG) multi-modal frequency features to classify the regions of stroke injury. The EEG signals were obtained from stroke patients and healthy subjects, who were divided into right-sided brain injury group, left-sided brain injury group, bilateral brain injury group, and healthy controls. First, the wavelet packet transform was used to perform a time-frequency analysis of the EEG signal and extracted a set of features (denoted as WPT features). Then, to explore the nonlinear phase coupling information of the EEG signal, phase-locked values (PLV) and partial directed correlations (PDC) were extracted from the brain network, and the brain network produced a second set of features noted as functional connectivity (FC) features. Furthermore, we fused the extracted multiple features and used the resnet50 convolutional neural network to classify the fused multi-modal (WPT + FC) features. Results: The classification accuracy of our proposed methods was up to 99.75%. Significance: The proposed multi-modal frequency features can be used as a potential indicator to distinguish regions of brain injury in stroke patients, and are potentially useful for the optimization of decoding algorithms for brain-computer interfaces.

Intratesticular Testosterone and Its Precursors among Azoospermic Men: A Pilot Study.

PURPOSE: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. MATERIALS AND METHODS: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. RESULTS: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. CONCLUSIONS: The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.