Saponin and Ribosome-Inactivating Protein Synergistically Trigger Lysosome-Dependent Apoptosis by Inhibiting Lysophagy: Potential to Become a New Antitumor Strategy.

The susceptibility of lysosomal membranes in tumor cells to cationic amphiphilic drugs (CADs) enables CADs to induce lysosomal membrane permeabilization (LMP) and trigger lysosome-dependent cell death (LDCD), suggesting a potential antitumor therapeutic approach. However, the existence of intrinsic lysosomal damage response mechanisms limits the display of the pharmacological activity of CADs. In this study, we report that low concentrations of QS-21, a saponin with cationic amphiphilicity extracted from Quillaja Saponaria tree, can induce LMP but has nontoxicity to tumor cells. QS-21 and MAP30, a type I ribosome-inactivating protein, synergistically induce apoptosis in tumor cells at low concentrations of both. Mechanistically, QS-21-induced LMP helps MAP30 escape from endosomes or lysosomes and subsequently enter the endoplasmic reticulum, where MAP30 downregulates the expression of autophagy-associated LC3 proteins, thereby inhibiting lysophagy. The inhibition of lysophagy results in the impaired clearance of damaged lysosomes, leading to the leakage of massive lysosomal contents such as cathepsins into the cytoplasm, ultimately triggering LDCD. In summary, our study showed that coadministration of QS-21 and MAP30 amplified the lysosomal disruption and can be a new synergistic LDCD-based antitumor therapy.

Evaluation of the feeding safety of Moringa (Moringa oleifera L.) in the Sprague Dawley rat.

This study aimed to evaluate the safety of Moringa by comparing the effects of different gavage doses of Moringa. The general behavior, body weight, food intake, blood indexes, serum biochemical indexes, and histopathology of rats were used to determine the safety threshold and to provide a reference for the further development and use of Moringa as animal feed. 40 Sprague Dawley rats were selected and given transoral gavage for 28 consecutive days. The T1, T2 and T3 groups were observed for general behavior, body weight, and food intake. Blood and serum biochemical indices were quantified, and histopathology was performed to evaluate the effect and safety of Moringa. The results of the toxicological test showed that (1) Only T1 groups experienced diarrhea. (2) The body weight and food intake of rats in each group were normal compared with the control group. (3) The hematological and serum biochemical indices of rats in the T1 group were significantly different from those of CK but were in the normal range; (4) The results of microscopic examination of the heart, liver, spleen, lung, and kidney of rats in each group were normal, but inflammation occurred in stomach and jejunum of rats in the T1 group, but not in the ileum. The gastrointestinal tract of rats in the T2 and T3 groups were normal. (5) No abnormal death occurred in any of the treatment groups.The results of this study revealed that gavage of Moringa homogenate at a dose of 6 g/kg BW can cause diarrhea in rats. Although there is no pathological effect on weight, food intake, blood and serum biochemical indicators in rats, there are pathological textures in the gastrointestinal tissue caused by diarrhea. Therefore, the safety threshold of Moringa homogenate should be ≤ 3 g/kg BW.

Extended Endoscopic Endonasal Approach for Giant Pediatric Craniopharyngiomas: Technical Note and Case Series.

BACKGROUND AND OBJECTIVES: Giant pediatric craniopharyngiomas are rare tumors whose clinical and surgical management is extremely challenging. A variety of open transcranial approaches has been used to resect these lesions. Although there has been an increasing acceptance of the endoscopic endonasal approach (EEA) for the resection of pediatric craniopharyngiomas in recent years, many surgeons continue to recommend against the use of the EEA for giant pediatric craniopharyngiomas. This study aimed to evaluate the feasibility of extended EEA for giant craniopharyngiomas in the pediatric population. METHODS: All consecutive pediatric patients with giant craniopharyngiomas (diameter >4 cm) who underwent endoscopic endonasal surgery at our institution were retrospectively reviewed. Data on demographic information, preoperative assessment, imaging features, surgical results, complications, and recurrences were recorded and analyzed. RESULTS: A total of 16 pediatric patients with an average age of 12 years were identified. The mean maximum diameter and volume of the tumors were 4.35 cm and 24.1 cm3, respectively. Gross total resection was achieved in 13 patients (81.3%) and subtotal resection in 3 patients (18.7%). Postoperatively, partial or complete anterior pituitary insufficiency occurred in 100% of patients, and 62.5% developed new-onset diabetes insipidus. Visual function improved in 9 patients (56.3%) and remained stable in 7 patients (43.7%). Postoperative cerebrospinal fluid leakage occurred in 2 patients and was successfully repaired through the EEA. During a mean follow-up of 44.3 months, 18.8% of patients had a >9% increase in body mass index, and 93.8% of patients successfully returned to school or work. Two patients (12.5%) suffered a recurrence. Disease control was achieved in 16 patients (100%) at final follow-up. CONCLUSION: The extended EEA allows adequate access to this challenging tumor and enables complete resection and visual improvement with a reasonable approach-related complication rate.

Chronic increasing nitrogen and endogenous phosphorus release from sediment threaten to the water quality in a semi-humid region reservoir.

The water quality in the drinking water reservoir directly affects people's quality of life and health. When external pollution input is effectively controlled, endogenous release is considered the main cause of water quality deterioration. As the major nitrogen (N) and phosphorus (P) sources in reservoirs, sediment plays a vital role in affecting the water quality. To understand the spatial and temporal variation of N and P in the sediment, this study analyzed the current characteristics and cumulative effects of a semi-humid reservoir, Yuqiao Reservoir, in North China. The N and P concentrations in the reservoir sediment were decreased along the flow direction, while the minimum values were recorded at the central sediment profile. External input and algal deposition were the main factors leading to higher sediment concentrations in the east (Re-E) and west (Re-W) areas of reservoir sediment profiles. According to the long-term datasets, the peaks of both sediment total nitrogen content and deposition rate were observed in the 2010s, which has increased about three times and six times than in the1990s, respectively. Therefore, the increase in phosphorus concentration may be the main reason for eutrophication in water in recent years. The mineralization of organic matter has a significant promoting effect on releasing N and P from sediments, which will intensify eutrophication in water dominated by P and bring huge challenges to water environment management. This study highlights that the current imbalance in N and P inputs into reservoirs and the endogenous P release from sediment will have a significant impact on water quality.

Bioprinted biomimetic hydrogel matrices guiding stem cell aggregates for enhanced chondrogenesis and cartilage regeneration.

Articular cartilage tissue has limited self-repair capabilities, with damage frequently progressing to irreversible degeneration. Engineered tissues constructed through bioprinting and embedded with stem cell aggregates offer promising therapeutic alternatives. Aggregates of bone marrow mesenchymal stromal cells (BMSCs) demonstrate enhanced and more rapid chondrogenic differentiation than isolated cells, thus facilitating cartilage repair. However, it remains a key challenge to precisely control biochemical microenvironments to regulate cellular adhesion and cohesion within bioprinted matrices simultaneously. Herein, this work reports a bioprintable hydrogel matrix with high cellular adhesion and aggregation properties for cartilage repair. The hydrogel comprises an enhanced cell-adhesive gelatin methacrylate and a cell-cohesive chitosan methacrylate (CHMA), both of which are subjected to photo-initiated crosslinking. By precisely adjusting the CHMA content, the mechanical stability and biochemical cues of the hydrogels are finely tuned to promote cellular aggregation, chondrogenic differentiation and cartilage repair implantation. Multi-layer constructs encapsulated with BMSCs, with high cell viability reaching 91.1%, are bioprinted and photo-crosslinked to support chondrogenic differentiation for 21 days. BMSCs rapidly form aggregates and display efficient chondrogenic differentiation both on the hydrogels and within bioprinted constructs, as evidenced by the upregulated expression of Sox9, Aggrecan and Collagen 2a1 genes, along with high protein levels. Transplantation of these BMSC-laden bioprinted hydrogels into cartilaginous defects demonstrates effective hyaline cartilage repair. Overall, this cell-responsive hydrogel scaffold holds immense promise for applications in cartilage tissue engineering.

Study on energy evolution and fractal characteristics of sandstone with different fracture dip angles under uniaxial compression.

In order to investigate the failure modes and instability mechanism of fractured rock. Uniaxial compression tests were conducted on sandstone specimens with different dip angles. Based on rock energy dissipation theory and fractal theory, the energy evolution characteristics and fragmentation fractal characteristics in the process of deformation and failure of specimens were analyzed. The results show that the peak strength and elastic modulus of fractured rock mass are lower than those of intact samples, and both show an exponential increase with the increase of fracture dip angle. The energy evolution laws of different fracture specimens are roughly similar and can be classified into four stages based on the stress-strain curve: pressure-tight, elastic, plastic, and post-destructive. The total strain energy, elastic strain energy, and dissipated strain energy of the specimen at the peak stress point increased exponentially with crack inclination, and the dissipated strain energy and compressive strength conformed to a power function growth relationship. The distribution of the fragments after the failure of the fracture sample has fractal characteristics, and the fractal dimension increases with the increase of the fracture dip angle. In addition, the higher the compressive strength of the specimen, the greater the energy dissipation, the more serious the degree of fragmentation, and the greater the fractal dimension. The data fitting further shows that there is a power function relationship between the dissipated strain energy and the fractal dimension. The research results can provide a theoretical basis for the stability of rock mass engineering and structural deformation control.

Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI.

Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Segmentation of parotid glands and tumors on magnetic resonance images is essential in accurately diagnosing and selecting appropriate surgical plans. However, segmentation of parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently, deep learning has developed rapidly, and Transformer-based networks have performed well on many computer vision tasks. However, Transformer-based networks have yet to be well used in parotid gland segmentation tasks. We collected a multi-center multimodal parotid gland MRI dataset and implemented parotid gland segmentation using a purely Transformer-based U-shaped segmentation network. We used both absolute and relative positional encoding to improve parotid gland segmentation and achieved multimodal information fusion without increasing the network computation. In addition, our novel training approach reduces the clinician's labeling workload by nearly half. Our method achieved good segmentation of both parotid glands and tumors. On the test set, our model achieved a Dice-Similarity Coefficient of 86.99%, Pixel Accuracy of 99.19%, Mean Intersection over Union of 81.79%, and Hausdorff Distance of 3.87. The purely Transformer-based U-shaped segmentation network we used outperforms other convolutional neural networks. In addition, our method can effectively fuse the information from multi-center multimodal MRI dataset, thus improving the parotid gland segmentation.

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.

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.

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.

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.

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)

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.

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.

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.

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.

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.

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.