Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models.

Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.

Genotype and phenotype in patients with ACAN gene variants: Three cases and literature review.

OBJECTIVE: To characterize the phenotype spectrum, diagnosis, and response to growth-promoting therapy in patients with ACAN variants causing familial short stature. METHODS: Three families with ACAN variants causing short stature were reported. Similar cases in the literature were summarized, and the genotype and phenotype were analyzed. RESULTS: Three novel heterozygous variants, c.757+1G>A, (splicing), c.6229delG, p.(Asp2078Tfs*1), and c.6679C>T, p.(Gln2227*) in the ACAN gene were identified. A total of 314 individuals with heterozygous variants from 105 families and 8 individuals with homozygous variants from 4 families were confirmed to have ACAN variants from literature and our 3 cases. Including our 3 cases, the variants reported comprised 33 frameshift, 39 missense, 23 nonsense, 5 splicing, 4 deletion, and 1 translocation variants. Variation points are scattered throughout the gene, while exons 12, 15, and 10 were most common (25/105, 11/105, and 10/105, respectively). Some identical variants existing in different families could be hot variants, c.532A>T, p.(Asn178Tyr), c.1411C>T, p.(Gln471*), c.1608C>A, p.(Tyr536*), c.2026+1G>A, (splicing), and c.7276G>T, p.(Glu2426*). Short stature, early-onset osteoarthritis, brachydactyly, midfacial hypoplasia, and early growth cessation were the common phenotypic features. The 48 children who received rhGH (and GnRHa) treatment had a significant height improvement compared with before (-2.18 ± 1.06 SD vs. -2.69 ± 0.95 SD, p < 0.001). The heights of children who received rhGH (and GnRHa) treatment were significantly improved compared with those of untreated adults (-2.20 ± 1.10 SD vs. -3.24 ± 1.14 SD, p < 0.001). CONCLUSION: Our study achieves a new understanding of the phenotypic spectrum, diagnosis, and management of individuals with ACAN variants. No clear genotype-phenotype relationship of patients with ACAN variants was found. Gene sequencing is necessary to diagnose ACAN variants that cause short stature. In general, appropriate rhGH and/or GnRHa therapy can improve the adult height of affected pediatric patients caused by ACAN variants.

Micro-environment triple-responsive hyaluronic acid hydrogel dressings to promote antibacterial activity, collagen deposition, and angiogenesis for diabetic wound healing.

The clinical treatment of chronic diabetic wounds is a long-standing thorny issue. Strategies targeting the diabetic micro-environment have been developed to promote wound healing. However, it remains challenging to reverse the adverse conditions and re-activate tissue regeneration and angiogenesis. In this work, we develop injectable hydrogels that are responsive to acidic conditions, reactive oxygen species (ROS), and high glucose levels in a diabetic wound micro-environment to sustainably deliver tannic acid (TA) to augment antibacterial, anti-inflammatory, and anti-oxidative activities. This triple-responsive mechanism is designed by introducing dynamic acylhydrazone and phenylboronic ester bonds to crosslink modified hyaluronic acid (HA) chains. At a diabetic wound, the acylhydrazone bonds may be hydrolyzed at low pH. Meanwhile, glucose may compete with TA, and ROS may oxidize the C-B bond to release TA. Thus, sustained release of TA is triggered by the diabetic micro-environment. The released TA effectively scavenges ROS and kills bacteria. In vivo experiments on diabetic mice demonstrate that the hydrogel dressing highly promotes angiogenesis and extracellular matrix (ECM) deposition, leading to eventual full healing of diabetic skin wounds. This micro-environment-triggered triple-responsive drug release provides a promising method for chronic diabetic wound healing.

Identification of Hsp20 gene family in Malus domestica and functional characterization of Hsp20 class I gene MdHsp18.2b.

Heat shock protein 20 (Hsp20) is a small molecule heat shock protein that plays an important role in plant growth, development, and stress resistance. Little is known about the function of Hsp20 family genes in apple (Malus domestica). Here, we performed a genome-wide analysis of the apple Hsp20 gene family, and a total of 49 Hsp20s genes were identified from the apple genome. Phylogenetic analysis revealed that the 49 genes were divided into 11 subfamilies, and MdHsp18.2b, a member located in the CI branch, was selected as a representative member for functional characterization. Treatment with NaCl and Botryosphaeria dothidea (B. dothidea), the causal agent of apple ring rot disease, significantly induced MdHsp18.2b transcription level. Further analysis revealed that overexpressing MdHsp18.2b reduced the resistance to salt stress but enhanced the resistance to B. dothidea infection in apple calli. Moreover, MdHsp18.2b positively regulated anthocyanin accumulation in apple calli. Physiology assays revealed that MdHsp18.2b promoted H2O2 production, even in the absence of stress factors, which might contribute to its functions in response to NaCl and B. dothidea infection. Hsps usually function as homo- or heterooligomers, and we found that MdHsp18.2b could form a heterodimer with MdHsp17.9a and MdHsp17.5, two members from the same branch with MdHsp18.2b in the phylogenetic tree. Therefore, we identified 49 Hsp20s genes from the apple genome and found that MdHsp18.2b was involved in regulating plant resistance to salt stress and B. dothidea infection, as well as in regulating anthocyanin accumulation in apple calli.

Tumor burden score and carcinoembryonic antigen predict outcomes in patients with intrahepatic cholangiocarcinoma following liver resection: a multi­institutional analysis.

BACKGROUND: The prognostic significance of tumor burden score (TBS) in relation to carcinoembryonic antigen (CEA) has not been investigated among patients undergoing hepatectomy for intrahepatic cholangiocarcinoma (ICC). This study aimed to develop and validate a simplified model, a combination of TBS and CEA (CTC grade), for predicting the long-term outcomes of postoperative ICC patients. METHODS: Patients who underwent curative - intent resection of ICC between 2011 and 2019 were identified from a large multi - institutional database. The impact of TBS, CEA, and the CTC grade on overall survival (OS) and recurrence - free survival (RFS) was evaluated in both the derivation and validation cohorts. The receiver operating characteristic curve was utilized for assessing the predictive accuracy of the model. Subgroup analyses were performed across 8th TNM stage system stratified by CTC grade to assess the discriminatory capacity within the same TNM stage. RESULTS: A total of 812 patients were included in the derivation cohort and 266 patients in the validation cohort. Survival varied based on CEA (low: 36.7% vs. high: 9.0%) and TBS (low: 40.3% vs. high: 17.6%) in relation to 5 - year survival (both p < 0.001). As expected, patients with low CTC grade (i.e., low TBS/low CEA) were associated with the best OS as well as RFS, while high CTC grade (i.e., high TBS/high CEA) correlated to the worst outcomes. The model exhibited well performance in both the derivation cohort (area under the curve of 0.694) and the validation cohort (0.664). The predictive efficacy of the CTC grade system remains consistently stable across TNM stages I and III/IV. CONCLUSION: The CTC grade, a composite parameter derived from the combination of TBS and CEA levels, served as an easy - to - use tool and performed well in stratifying patients with ICC relative to OS and RFS.

OsKASI-2 is required for the regulation of unsaturation levels of membrane lipids and chilling tolerance in rice.

Chilling stress has seriously limited the global production and geographical distribution of rice. However, the molecular mechanisms associated with plant responses to chilling stress are less known. In this study, we revealed a member of ß-ketoacyl-ACP synthase I family (KASI), OsKASI-2 which confers chilling tolerance in rice. OsKASI-2 encodes a chloroplast-localized KASI enzyme mainly expressed in the leaves and anthers of rice and strongly induced by chilling stress. Disruption of OsKASI-2 led to decreased KAS enzymatic activity and the levels of unsaturated fatty acids, which impairs degree of unsaturation of membrane lipids, thus increased sensitivity to chilling stress in rice. However, the overexpression of OsKASI-2 significantly improved the chilling tolerance ability in rice. In addition, OsKASI-2 may regulate ROS metabolism in response to chilling stress. Natural variation of OsKASI-2 might result in difference in chilling tolerance between indica and japonica accessions, and Hap1 of OsKASI-2 confers chilling tolerance in rice. Taken together, we suggest OsKASI-2 is critical for regulating degree of unsaturation of membrane lipids and ROS accumulation for maintenance of membrane structural homeostasis under chilling stress, and provide a potential target gene for improving chilling tolerance of rice.

All-In-One OsciDrop Digital PCR System for Automated and Highly Multiplexed Molecular Diagnostics.

Digital PCR (dPCR) holds immense potential for precisely detecting nucleic acid markers essential for personalized medicine. However, its broader application is hindered by high consumable costs, complex procedures, and restricted multiplexing capabilities. To address these challenges, an all-in-one dPCR system is introduced that eliminates the need for microfabricated chips, offering fully automated operations and enhanced multiplexing capabilities. Using this innovative oscillation-induced droplet generation technique, OsciDrop, this system supports a comprehensive dPCR workflow, including precise liquid handling, pipette-based droplet printing, in situ thermocycling, multicolor fluorescence imaging, and machine learning-driven analysis. The system's reliability is demonstrated by quantifying reference materials and evaluating HER2 copy number variation in breast cancer. Its multiplexing capability is showcased with a quadruplex dPCR assay that detects key EGFR mutations, including 19Del, L858R, and T790M in lung cancer. Moreover, the digital stepwise melting analysis (dSMA) technique is introduced, enabling high-multiplex profiling of seven major EGFR variants spanning 35 subtypes. This innovative dPCR system presents a cost-effective and versatile alternative, overcoming existing limitations and paving the way for transformative advances in precision diagnostics.

Identification of novel protein biomarkers from the blood and urine for the early diagnosis of bladder cancer via proximity extension analysis.

BACKGROUND: Bladder cancer (BC) is a very common urinary tract malignancy that has a high incidence and lethality. In this study, we identified BC biomarkers and described a new noninvasive detection method using serum and urine samples for the early detection of BC. METHODS: Serum and urine samples were retrospectively collected from patients with BC (n = 99) and healthy controls (HC) (n = 50), and the expression levels of 92 inflammation-related proteins were examined via the proximity extension analysis (PEA) technique. Differential protein expression was then evaluated by univariate analysis (p < 0.05). The expression of the selected potential marker was further verified in BC and adjacent tissues by immunohistochemistry (IHC) and single-cell sequencing. A model was constructed to differentiate BC from HC by LASSO regression and compared to the detection capability of FISH. RESULTS: The univariate analysis revealed significant differences in the expression levels of 40 proteins in the serum (p < 0.05) and 17 proteins in the urine (p < 0.05) between BC patients and HC. Six proteins (AREG, RET, WFDC2, FGFBP1, ESM-1, and PVRL4) were selected as potential BC biomarkers, and their expression was evaluated at the protein and transcriptome levels by IHC and single-cell sequencing, respectively. A diagnostic model (a signature) consisting of 14 protein markers (11 in serum and three in urine) was also established using LASSO regression to distinguish between BC patients and HC (area under the curve = 0.91, PPV = 0.91, sensitivity = 0.87, and specificity = 0.82). Our model showed better diagnostic efficacy than FISH, especially for early-stage, small, and low-grade BC. CONCLUSION: Using the PEA method, we identified a panel of potential protein markers in the serum and urine of BC patients. These proteins are associated with the development of BC. A total of 14 of these proteins can be used to detect early-stage, small, low-grade BC. Thus, these markers are promising for clinical translation to improve the prognosis of BC patients.

Elucidation of the dominant factors influencing N2O emission in water-level fluctuation zones in a karst canyon reservoir, southwest China.

The water-level fluctuations zones (WLFZs) are crucial transitional interfaces within river-reservoir systems, serving as hotspots for N2O emission. However, the comprehension of response patterns and mechanisms governing N2O emission under hydrological fluctuation remains limited, especially in karstic canyon reservoirs, which introduces significant uncertainty to N2O flux assessments. Soil samples were collected from the WLFZs of the Hongjiadu (HJD) Reservoir along the water flow direction from transition zone (T1 and T2) to lacustrine zone (T3, T4 and T5) at three elevations for each site. These soil columns were used to conduct simulation experiments under various water-filled pore space gradients (WFPSs) to investigate the potential N2O flux pattern and elucidate the underlying mechanism. Our results showed that nutrient distribution and N2O flux pattern differed significantly between two zones, with the highest N2O fluxes in the transition zone sites and lacustrine zone sites were found at 75 % and 95 % WFPS, respectively. Soil nutrient loss in lower elevation areas is influenced by prolonged impoundment durations. The higher N2O fluxes in the lacustrine zone can be attributed to increased nutrient levels resulting from anthropogenic activities. Furthermore, correlation analysis revealed that soil bulk density significantly impacted N2O fluxes across all sites, while NO3-and SOC facilitated N2O emissions in T1-T2 and T4-T5, respectively. It was evident that N2O production primarily contributed to nitrification in the transition zone and was constrained by the mineralization process, whereas denitrification dominated in the lacustrine zone. Notably, the annual N2O efflux from WLFZs accounted for 27 % of that from the water-air interface in HJD Reservoir, indicating a considerably lower contribution than anticipated. Nevertheless, this study highlights the significance of WLFZs as a vital potential source of N2O emission, particularly under the influence of anthropogenic activities and high WFPS gradient.

An exploratory view into allelic drop-out of sequenced autosomal STRs.

As massively parallel sequencing is implemented in forensic genetics, an understanding of sequence data must accompany these advancements, that is, accurate modeling of data for proper statistical analysis. Allelic drop-out, a common stochastic effect seen in genetic data, is often modeled in statistical analysis of STR results. This proof-of-concept study sequenced several serial dilutions of a standard sample ranging from 4 ng to 7.82 pg to evaluate allelic drop-out trends on a select panel of autosomal STRs using the ForenSeq™ DNA Signature Prep Kit, Primer Set A on the Illumina MiSeq FGx. Parameters assessed included locus, profile, and run specific information. A majority of the allelic drop-out occurred in DNA concentrations less than 31.25 pg. Statistical results indicated a need for locus-specific modeling based on STR descriptors, like simple versus compound repeat patterns. No correlation was seen between average read count of scored alleles and allelic drop-out at a locus. A statistical correlation was observed between the amount of allelic drop-out and the starting amount of DNA in a sample, average read count of a sample, and total read count generated on a flow cell. This study supports using common allelic drop-out factors used in fragment length analysis on sequenced STRs while including additional locus, sample, and run specific information. Results demonstrate multiple factors that can be considered when developing probability of allelic drop-out models for sequenced autosomal STRs including locus-specific analysis, total read count of a profile, and total read count sequenced on a flow cell.

A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study

Purpose Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algorithms to develop a prediction model for the prognosis of patients with long bone sarcoma. Methods Data of patients with long bone sarcoma in the extremities was collected from the Surveillance, Epidemiology, and End Results Program database from 2004 to 2014. Univariate and multivariate analyses were performed to select possible prediction features. DeepSurv, a deep learning model, was constructed for predicting cancer-specific survival rates. In addition, the classical cox proportional hazards model was established for comparison. The predictive accuracy of our models was assessed using the C-index, Integrated Brier Score, receiver operating characteristic curve, and calibration curve. Results Age, tumor extension, histological grade, tumor size, surgery, and distant metastasis were associated with cancer-specific survival in patients with long bone sarcoma. According to loss function values, our models converged successfully and effectively learned the survival data of the training cohort. Based on the C-index, area under the curve, calibration curve, and Integrated Brier Score, the deep learning model was more accurate and flexible in predicting survival rates than the cox proportional hazards model. Conclusion A deep learning model for predicting the survival probability of patients with long bone sarcoma was constructed and validated. It is more accurate and flexible in predicting prognosis than the classical CoxPH model (AU)

Functions of Phytochrome Interacting Factors (PIFs) in Adapting Plants to Biotic and Abiotic Stresses.

Plants possess the remarkable ability to sense detrimental environmental stimuli and launch sophisticated signal cascades that culminate in tailored responses to facilitate their survival, and transcription factors (TFs) are closely involved in these processes. Phytochrome interacting factors (PIFs) are among these TFs and belong to the basic helix-loop-helix family. PIFs are initially identified and have now been well established as core regulators of phytochrome-associated pathways in response to the light signal in plants. However, a growing body of evidence has unraveled that PIFs also play a crucial role in adapting plants to various biological and environmental pressures. In this review, we summarize and highlight that PIFs function as a signal hub that integrates multiple environmental cues, including abiotic (i.e., drought, temperature, and salinity) and biotic stresses to optimize plant growth and development. PIFs not only function as transcription factors to reprogram the expression of related genes, but also interact with various factors to adapt plants to harsh environments. This review will contribute to understanding the multifaceted functions of PIFs in response to different stress conditions, which will shed light on efforts to further dissect the novel functions of PIFs, especially in adaption to detrimental environments for a better survival of plants.

From macro- to nano- scales: Effect of fibrillary celluloses from okara on performance of edible starch film.

Starch/cellulose composite is one of the most promising systems since both matrix and reinforce agent have same chemical unite glucose, which results in an excellent compatibility. In this work, edible starch film was developed by compositing starch with diverse fibrillary celluloses (FCs) derived from okara, employing a confluence of chemical interactions and mechanical influences. Since diameter of the FCs can be easily controlled by processing methodologies, it is the first time to systematically investigate the effect of diameter of the FCs from macro to nano-scales on the performances of starch-based film. The fabricated macro- and nano-fibrillar celluloses and reinforced starch films were characterized by scanning electron microscope, optical microscopy, Fourier transform infrared spectroscopy, Rheometer and contact angle. Results showed that the FCs increased modulus (about 170 %) and tensile strength (about 180 %) significantly as expected since they are well-compatible and some chemical interactions. It was found that nano-fibrillary celluloses (CNFs) improve the toughness (about 20 %) of the starch film more efficiently, which improved the well-recognized weakness of starch-based materials. The nano-scale roughness on the surface of the starch film caused by different shrinkage ratios between starch and CNFs during drying reduced water sensitivity, which is another well-recognized weakness of starch film.

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

Machine learning and experiments revealed a novel pyroptosis-based classification linked to diagnosis and immune landscape in spinal cord injury.

Background: Rising evidence indicates the development of pyroptosis in the initiation and pathogenesis of spinal cord injury (SCI). However, the associated effects of pyroptosis-related genes (PRGs) in SCI are unclear. Methods: We obtained the gene expression profiles of SCI and normal samples in the GEO. Database: The R package limma screened for differentially expressed (DE) PRGs and performed functional enrichment analysis. Mechanical learning and PPI analysis helped filter essential PRGs to diagnose SCI. Peripheral blood was collected for validation from ten SCI patients and eight healthy individuals. The association of essential PRGs with immune infiltration was evaluated, and pyroptosis subtypes were recognized in SCI patients by unsupervised cluster analysis. Besides, a SCI model was built for in vivo validation of essential PRGs. Result: We identified 25 DE-PRGs between SCI and normal controls. Functional enrichment analysis revealed the principal involvement of DE-PRGs in pyroptosis, inflammasome complex, interleukin-1 beta production, etc. Subsequently, three essential PRGs were identified and validated, showing excellent diagnostic efficacy and significant correlation with immune cell infiltration. Additionally, we developed diagnostic nomograms to predict the occurrence of SCI. Two pyroptosis subtypes exhibited distinct biological functions and immune landscapes among SCI patients. Finally, the expression of these essential PRGswas verified in vivo. Conclusion: The current study described the vital effects of pyroptosis-related genes in SCI, providing a novel direction for effective assessment and management of SCI.

Increased risk of periprosthetic joint infection after traumatic injury in joint revision patients.

BACKGROUND: Periprosthetic joint infection (PJI) is a serious complication after total joint arthroplasty (TJA). Although some risk factors of PJI were well studied, the association between trauma and PJI remains unknown in revision patients. MATERIALS AND METHODS: Between 2015 and 2018, a total of 71 patients with trauma history before revisions (trauma cohort) were propensity score matched (PSM) at a ratio of 1 to 5 with a control cohort of revision patients without a history of trauma. Then, the cumulative incidence rate of PJI within 3 years after operation between the two groups was compared. The secondary endpoints were aseptic revisions within 3 postoperative years, complications up to 30 postoperative days, and readmission up to 90 days. During a minimal 3-year follow-up, the survival was comparatively analyzed between the trauma cohort and the control cohort. RESULTS: The cumulative incidence of PJI was 40.85% in patients with trauma history against 27.04% in the controls (P = 0.02). Correspondingly, the cumulative incidence of aseptic re-revisions was 12.68% in patients with trauma history compared with 5.07% in the control cohort (P = 0.028). Cox regression revealed that trauma history was a risk factor of PJI (HR, 1.533 [95%CI, (1.019,2.306)]; P = 0.04) and aseptic re-revisions (HR, 3.285 [95%CI, (1.790,6.028)]; P < 0.0001). CONCLUSIONS: Our study demonstrated that revision patients with trauma history carried a higher risk of PJI compared to those without trauma history. Moreover, after revisions, the trauma patients were still at higher risk for treatment failure due to PJI, periprosthetic joint fracture, and mechanical complications.

Identification of immune infiltration and immune-related biomarkers of periprosthetic joint infection.

Background: The immune response associated with periprosthetic joint infection (PJI) is an emerging but relatively unexplored topic. The aim of this study was to investigate immune cell infiltration in periprosthetic tissues and identify potential immune-related biomarkers. Methods: The GSE7103 dataset from the GEO database was selected as the data source. Differentially expressed genes (DEGs) and significant modular genes in weighted correlation network analysis (WGCNA) were identified. Functional enrichment analysis and transcription factor prediction were performed on the overlapping genes. Next, immune-related genes from the ImmPort database were matched. The protein-protein interaction (PPI) analysis was performed to identify hub genes. CIBERSORTx was used to evaluate the immune cell infiltration pattern. Spearman correlation analysis was used to evaluate the relationship between hub genes and immune cells. Results: A total of 667 DEGs were identified between PJI and control samples, and 1847 PJI-related module genes were obtained in WGCNA. Enrichment analysis revealed that the common genes were mainly enriched in immune and host defense-related terms. TFEC, SPI1, and TWIST2 were the top three transcription factors. Three hub genes, SDC1, MMP9, and IGF1, were identified in the immune-related PPI network. Higher levels of plasma cells, CD4+ memory resting T cells, follicular helper T cells, resting mast cells, and neutrophils were found in the PJI group, while levels of M0 macrophages were lower. Notably, the expression of all three hub genes correlated with the infiltration levels of seven types of immune cells. Conclusion: The present study revealed immune infiltration signatures in the periprosthetic tissues of PJI patients. SDC1, MMP9, and IGF1 were potential immune-related biomarkers for PJI.

Continuous age- and sex-specific reference ranges of liver enzymes in Chinese children and application in pediatric non-alcoholic fatty liver disease.

BACKGROUND: Alanine aminotransferase (ALT) is widely used to screen patients with hepatic diseases. However, the current reference ranges (< 50 U/L) were developed by laboratories and have not been validated in populations with a large number of healthy individuals. METHODS: This study collected venous blood and anthropometric data from a total of 13,287 healthy children aged 3 months to 18 years who underwent routine physical examinations in the Department of Pediatric Healthcare. We applied the least mean square algorithm to establish age- and sex-related reference percentiles of serum levels of transaminases. For validation, we recruited 4276 children and adolescents with obesity/overweight who underwent evaluation and metabolic tests in the hospital. Using receiver operating characteristic curves, we determined age- and sex-specific upper limit percentiles of liver enzymes for fatty liver diseases. RESULTS: This study revealed a significant correlation between serum transaminase levels and age and sex (P < 0.01). These transaminase levels exhibited age- and sex-specific patterns. Among individuals in the non-alcoholic fatty liver disease (NAFLD) cohort, elevated ALT levels displayed a positive association with clinical markers of disease severity, including homeostatic model assessment of insulin resistance, waist-hip ratio, and serum uric acid levels (P < 0.01). According to the receiver operating characteristic curves, ALT levels at the 92.58th percentile for boys and the 92.07th percentile for girls yielded the highest accuracy and specificity. CONCLUSIONS: This study provides age- and sex-specific reference ranges for ALT, aspartate aminotransferase, and γ-glutamyltransferase in Chinese children and adolescents, making it the largest population study to date. Furthermore, the study establishes a precise upper limit for ALT levels, facilitating their use in NAFLD screening. Video Abstract.

The Role of Depression and Anxiety in the Relationship Between Arthritis and Cognitive Impairment in Chinese Older Adults.

BACKGROUND: Mental disorders and cognitive impairment are common in older patients with arthritis. While it is recognized that mental conditions may play a role in the connection between arthritis and cognitive impairment, the precise underlying relationship remains uncertain. METHODS: The data was derived from the baseline survey of the Guangdong Mental Health Survey in South China, involving a sample of 3,764 citizens aged 65 and older. An array of aspects were explored, including socio-demographics, lifestyle behaviors, self-reported chronic conditions, depression, anxiety, and cognitive impairment. Logistic regression analyses examined the association between arthritis and cognitive impairment after adjustment for potential confounders. Serial mediation models were used to examine whether depression or anxiety played a mediating role in the arthritis-cognitive impairment linkage. RESULTS: The prevalence rates of cognitive impairment and arthritis of the older adults were 28.9% and 12.1%, respectively. Compared to those without arthritis, participants with arthritis were at a higher risk of cognitive impairment (OR = 1.322, 95%CI: 1.022-1.709) after adjustment for socio-demographics, lifestyle behaviors, and mental health conditions. Serial mediation analyses indicated that depressive and anxiety symptoms co-played a serial mediating role in the association between arthritis and cognitive impairment (B1 = 0.025, 95%CI: 0.005-0.052; B2 = 0.050, 95%CI: 0.021-0.086). CONCLUSIONS: Arthritis may heighten cognitive impairment risk in Chinese older adults, and the relationship was potentially mediated by depressive and anxiety symptoms. Future interventions should be considered, integrating mental health assessments into arthritis care frameworks and being alert to possible cognitive impairment.

Cation-crosslinkedκ-carrageenan sub-microgel medium for high-quality embedded bioprinting.

Three-dimensional (3D) bioprinting embedded within a microgel bath has emerged as a promising strategy for creating intricate biomimetic scaffolds. However, it remains a great challenge to construct tissue-scale structures with high resolution by using embedded 3D bioprinting due to the large particle size and polydispersity of the microgel medium, as well as its limited cytocompatibility. To address these issues, novel uniform sub-microgels of cell-friendly cationic-crosslinked kappa-carrageenan (κ-Car) are developed through an easy-to-operate mechanical grinding strategy. Theseκ-Car sub-microgels maintain a uniform submicron size of around 642 nm and display a rapid jamming-unjamming transition within 5 s, along with excellent shear-thinning and self-healing properties, which are critical for the high resolution and fidelity in the construction of tissue architecture via embedded 3D bioprinting. Utilizing this new sub-microgel medium, various intricate 3D tissue and organ structures, including the heart, lungs, trachea, branched vasculature, kidney, auricle, nose, and liver, are successfully fabricated with delicate fine structures and high shape fidelity. Moreover, the bone marrow mesenchymal stem cells encapsulated within the printed constructs exhibit remarkable viability exceeding 92.1% and robust growth. Thisκ-Car sub-microgel medium offers an innovative avenue for achieving high-quality embedded bioprinting, facilitating the fabrication of functional biological constructs with biomimetic structural organizations.