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BACKGROUND: Rituximab resistance is one of the great challenges in the treatment of diffuse large B-cell lymphoma (DLBCL), but relevant biomarkers and signalling pathways remain to be identified. Coptis chinensis and its active ingredients have antitumour effects; thus, the potential bioactive compounds and mechanisms through which Coptis chinensis acts against rituximab-resistant DLBCL are worth exploring. OBJECTIVE: To elucidate the core genes involved in rituximab-resistant DLBCL and the potential therapeutic targets of candidate monomers of Coptis chinensis. METHODS: Using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), the Similarity Ensemble Approach and Swiss Target Prediction, the main ingredients and pharmacological targets of Coptis chinensis were identified through database searches. Through the overlap between the pharmacological targets of Coptis chinensis and the core targets of rituximab-resistant DLBCL, we identified the targets of Coptis chinensis against rituximab-resistant DLBCL and constructed an active compound-target interaction network. The targets and their corresponding active ingredients of Coptis chinensis against rituximab-resistant DLBCL were molecularly docked. RESULTS: Berberine, quercetin, epiberberine and palmatine, the active components of Coptis chinensis, have great potential for improving rituximab-resistant DLBCL via PIK3CG. CONCLUSION: This study revealed biomarkers and Coptis chinensis-associated molecular functions for rituximab-resistant DLBCL. [ABSTRACT FROM AUTHOR]

Uncontrollable bleeding is still the main cause of post-traumatic deaths due to the blood loss. Moreover, infectious complication of wound is also still a challenging problem for wound healing. Nevertheless, the currently available hemostasis drugs or materials cannot stanch bleeding well due to single function, slow in effectiveness, adhere to wounds easily, poor gas permeability, etc. Therefore, it is of a great significance to utilize a biomedical hemostatic material that can stop bleeding quickly, preventing from bacterial infections, and with good biocompatibility properties. Herein chitosan (CS) was modified with gallic acid (GA) and thrombin (TB) to prepare an antibacterial hemostatic composite dressing. The CS-based composite hydrogel dressing was obtained by acylation modification, ultraviolet curing crosslinking method and physical mixing. The in vitro results showed that our prepared CS-based composite hydrogel has obvious burst release and good degradation property. Moreover, the in vivo results showed that it has a strong antibacterial property that is much better than single CS, and it can stop bleeding in 1 min which can promote wound healing. Therefore, the findings of this study is expected to contribute to the future designing of biomedical hemostatic materials with improved properties.

Simple Summary: A new species of odd-scaled snake in the genus Achalinus is described from Dabie Mountains Luan City, Anhui Province, China, based on one male and two female specimens. Bayesian inference and maximum likelihood analyses based on a mitochondrial DNA fragment (CO1) indicated the new taxon is different from its congeners (p–distance ≥ 9.4%). Morphologically, the new species can be diagnosed from the other species by a combination of 12 characters. The recognition of the new species brings the number of described Achalinus species to 22. A new species of Xenodermid snake, Achalinus dabieshanensis sp. nov., was described based on three specimens (two female and one male) collected from the Dabie Mountains of western Anhui Province. It can be distinguished from known congeners by a significant genetic divergence in the mitochondrial gene fragment COI (p-distance ≥ 9.4%) and the following combination of characteristics: (1) length of the suture between the internasals being distinctly shorter than between the prefrontals; (2) a single loreal; (3) dorsal scales strongly keeled, in 23 rows throughout the body; (4) two pairs of prefrontals; (5) six supralabials; (6) five infralabials; (7) temporals 2 + 2 + 3 (or 2 + 2 + 4); (8) 141–155 ventrals; (9) 45–55 subcaudals, unpaired; (10) anal entire; (11) weakly iridescent tinged, uniform, brown to black dorsum with vertebral scales and about three adjacent dorsal scales dark brown forming a longitudinal vertebral line from posterior margin of parietals to tail tip; (12) light brown venter, ventral shields wide, visible on both sides, light brown flanks, giving the appearance of a black subcaudal streak. The recognition of the new species increases the number of described Achalinus species to 22. [ABSTRACT FROM AUTHOR]

Aim: To provide quality care to older adults, healthcare professionals should be aware that osteoporotic vertebral compression fractures (OVCFs) might occur sequentially in the same patient, involving different vertebral bodies, each separated by short intervals. This situation is called chronologically clustered OVCFs (CCOVCF). Methods: A total of 40 patients with CCOVCFs (index cohort) were retrospectively analyzed, and compared with 40 patients having only one OVCF (comparison cohort). All fractures were treated with percutaneous balloon kyphoplasty. Results: In the index cohort, the number of patients having the second, third, fourth and fifth OVCF events within 3 months were 40, 15, five and two, respectively. Recurring pain or seemingly non‐stop pain were the major reasons why new OCVFs were found. The average interval between pain relief provided by percutaneous balloon kyphoplasty and radiographic diagnosis of new OVCFs was significantly longer than that between pain relief and a new episode of disabling pain (26.7 ± 16.8 vs 16.4 ± 15.8 days, P < 0.0001), reflecting how shortly new OCVFs occurred after successful surgery, and how often they were neglected. The mean T‐score of the index cohort was significantly lower than that of the comparison cohort (−3.66 ± 0.79 vs −3.17 ± 0.80, P = 0.01). Conclusions: CCOVCFs make a patient seem constantly in pain, despite repeated admissions and operations. Recurrent symptoms after an effective procedure should be taken as a warning that a new OCVF might have occurred, even if only a few days apart. Advanced osteoporosis is a significant risk factor for CCOVCFs. Geriatr Gerontol Int 2023; 23: 44–49. [ABSTRACT FROM AUTHOR]

Bearings, as the key mechanical components of rotary machinery, are widely used in modern aerospace equipment, such as helicopters and aero-engines. Intelligent fault diagnosis, as the main function of prognostic health management systems, plays a critical role in maintaining equipment safety in aerospace applications. Recently, data-driven intelligent diagnosis approaches have achieved great success due to the availability of large-scale, high-quality, and complete labeled data. However, in a real application, labeled data is often scarce because it requires manual labeling, which is time-consuming and labor-intensive. Meanwhile, health monitoring data are usually scattered in different regions or equipment in the form of data islands. Traditional fault diagnosis techniques fail to gather enough data for model training due to data security, economic conflict, relative laws, and other reasons. Therefore, it is a challenge to effectively combine the data advantages of different equipment to develop an intelligent diagnosis model with better performance. To address this issue, a novel clustering federated learning (CFL) method with a self-attention mechanism is proposed for bearing fault diagnosis. Firstly, a deep neural network with a self-attention mechanism is developed in a convolutional pipe for feature extraction, which can capture local and global information from raw input. Then, the CFL is further constructed to gather the data from different equipment with similar data distribution in an unsupervised manner. Finally, the CFL-based diagnosis model can be well trained by fully utilizing the distributed data, while ensuring data privacy safety. Experiments are carried out with three different bearing datasets in aerospace applications. The effectiveness and the superiority of the proposed method have been validated compared with other popular fault diagnosis schemes. [ABSTRACT FROM AUTHOR]

Opioid overdose suppresses brainstem respiratory circuits, causes apnoea and may result in death. Epidural electrical stimulation (EES) at the cervical spinal cord facilitated motor activity in rodents and humans, and we hypothesized that EES of the cervical spinal cord could antagonize opioid‐induced respiratory depression in humans. Eighteen patients requiring surgical access to the dorsal surface of the spinal cord between C2 and C7 received EES or sham stimulation for up to 90 s at 5 or 30 Hz during complete (OFF‐State) or partial suppression (ON‐State) of respiration induced by remifentanil. During the ON‐State, 30 Hz EES at C4 and 5 Hz EES at C3/4 increased tidal volume and decreased the end‐tidal carbon dioxide level compared to pre‐stimulation control levels. EES of 5 Hz at C5 and C7 increased respiratory frequency compared to pre‐stimulation control levels. In the OFF‐State, 30 Hz cervical EES at C3/4 terminated apnoea and induced rhythmic breathing. In cadaveric tissue obtained from a brain bank, more neurons expressed both the neurokinin 1 receptor (NK1R) and somatostatin (SST) in the cervical spinal levels responsive to EES (C3/4, C6 and C7) compared to a region non‐responsive to EES (C2). Thus, the capacity of cervical EES to oppose opioid depression of respiration may be mediated by NK1R+/SST+ neurons in the dorsal cervical spinal cord. This study provides proof of principle that cervical EES may provide a novel therapeutic approach to augment respiratory activity when the neural function of the central respiratory circuits is compromised by opioids or other pathological conditions. Key points: Epidural electrical stimulation (EES) using an implanted spinal cord stimulator (SCS) is an FDA‐approved method to manage chronic pain.We tested the hypothesis that cervical EES facilitates respiration during administration of opioids in 18 human subjects who were treated with low‐dose remifentanil that suppressed respiration (ON‐State) or high‐dose remifentanil that completely inhibited breathing (OFF‐State) during the course of cervical surgery.Dorsal cervical EES of the spinal cord augmented the respiratory tidal volume or increased the respiratory frequency, and the response to EES varied as a function of the stimulation frequency (5 or 30 Hz) and the cervical level stimulated (C2–C7).Short, continuous cervical EES restored a cyclic breathing pattern (eupnoea) in the OFF‐State, suggesting that cervical EES reversed the opioid‐induced respiratory depression.These findings add to our understanding of respiratory pattern modulation and suggest a novel mechanism to oppose the respiratory depression caused by opioids. [ABSTRACT FROM AUTHOR]

Simultaneous fault type and severity identification is critical for timely maintenance actions to prevent accidents from industrial machinery. The former can indicate occurrences of specific faults, and the latter can track early fault evolutions. Existing methods generally assume training and testing data are drawn from the same data distribution. However, in real industries, due to the change of working conditions, domain shift phenomenon can be triggered. The existing intelligent diagnosis methods are less effective in such scenarios for lack of domain adaptation ability. To address such problems, a novel two-branch domain adaptation network is developed. A deep convolutional neural network with two branches, as the main hierarchical architecture, is designed to handle two recognition tasks. The maximum mean discrepancy based multi-kernel learning is embedded to reduce the distribution discrepancy between the source domain and target domain. As such, the domain-invariant features with a hierarchical structure can be effectively extracted and the fault types and fault severities can be recognized at the same time. Experiments on a bearing dataset, a gear dataset and a motor bearing dataset are carried out to validate the effectiveness of the proposed approach. The results demonstrate that the proposed method can effectively perform fault type and severity identification simultaneously and obviously outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pharmaceutical wastewater is a kind of high‐hazardous waste. To realize non‐waste production, experiment and computational fluid dynamics (CFD) simulation were performed to reveal the hydraulic mechanism of mixing modes. Three digesters, numbered 0# (no‐port), 1# (one‐port), and 4# (four‐port), were conducted by dextran pharmaceutical wastewater. Digester 0# is the control group without mixing. Mixing mode of bottom inlet and high‐position outlet is employed to 1# and 4#, the outlets of 1# and 4# are centralized outlet with only one port and distributed outlets with four ports, respectively. Experiment result shows the daily biogas production of 1# and 4# are 45% and 58% higher than 0#, and the pollutants removal rate increased 20% and 24%, respectively. CFD simulation shows the second phase (dextran wastewater) of 4# failing to form a complete hydraulic path like the first phase (water), which explain the mixing modes can greatly improve the biogas yield, but the four‐port mode has a weaker advantage than the one‐port. [ABSTRACT FROM AUTHOR]

Background Epidemiologic studies examining the relations between dairy product and calcium intakes and breast cancer have been inconclusive, especially for tumor subtypes. Objective To evaluate the associations between intakes of specific dairy products and calcium and risk of breast cancer overall and for subtypes defined by estrogen receptor (ER) status. Method We pooled the individual-level data of over 1 million women who were followed for a maximum of 8–20 years across studies. Associations were evaluated for dairy product and calcium intakes and risk of incident invasive breast cancer overall (n  = 37,861 cases) and by subtypes defined by ER status. Study-specific multivariable hazard ratios (HRs) were estimated and then combined using random-effects models. Results Overall, no clear association was observed between the consumption of specific dairy foods, dietary (from foods only) calcium, and total (from foods and supplements) calcium, and risk of overall breast cancer. Although each dairy product showed a null or very weak inverse association with risk of overall breast cancer (P , test for trend >0.05 for all), differences by ER status were suggested for yogurt and cottage/ricotta cheese with associations observed for ER-negative tumors only (pooled HR = 0.90, 95% CI: 0.83, 0.98 comparing ≥60 g/d with <1 g/d of yogurt and 0.85, 95% CI: 0.76, 0.95 comparing ≥25 g/d with <1 g/d of cottage/ricotta cheese). Dietary calcium intake was only weakly associated with breast cancer risk (pooled HR = 0.98, 95% CI: 0.97, 0.99 per 350 mg/d). Conclusion Our study shows that adult dairy or calcium consumption is unlikely to associate with a higher risk of breast cancer and that higher yogurt and cottage/ricotta cheese intakes were inversely associated with the risk of ER-negative breast cancer, a less hormonally dependent subtype with poor prognosis. Future studies on fermented dairy products, earlier life exposures, ER-negative breast cancer, and different racial/ethnic populations may further elucidate the relation. [ABSTRACT FROM AUTHOR]

In recent years, deep learning methods, especially convolutional neural network (CNN), have received extensive attentions and applications in fault diagnosis. However, recent studies have shown that the shift-invariance of CNN is not good enough, resulting in fragile feature extraction and sharp reduction in model performance when the shift occurs in the input. To improve the shift-invariance of CNN, considering the periodic characteristics of vibration signals, a multiscale CNN with feature alignment (MSCNN-FA) is proposed for bearing fault diagnosis under different working conditions. First, by analyzing the operating principles of the convolutional layer and pooling layer, a feature alignment module including single-stride convolution layer, adaptive max-pooling layer, and global average pooling layer is designed to obtain aligned features. Next, to extract shift-invariant robust features from vibration signals, a multiscale convolution strategy is utilized, and a feature-aligned multiscale feature extractor is constructed. Finally, a classifier composed of fully connected (FC) layers is constructed for bearing fault diagnosis. The effectiveness of the method is verified by a rolling bearing experiment, which outperforms other related existing CNN-based methods in terms of diagnosis accuracy and feature robustness. [ABSTRACT FROM AUTHOR]

Recently, deep transfer learning based intelligent fault diagnosis has been widely investigated, and the tasks that source and target domains share the same fault categories have been well addressed. However, due to complexity and uncertainty of mechanical equipment, unknown new faults may occur unexpectedly. This problem has received less attention in the current research, which seriously limited the application of deep transfer learning. In this article, a two-stage transfer adversarial network is proposed for multiple new faults detection of rotating machinery. First, a novel deep transfer learning model is constructed based on an adversarial learning strategy, which can effectively separate multiple unlabeled new fault types from labeled known ones. Second, an unsupervised convolutional autoencoders model with silhouette coefficient is built to recognize the number of new fault types. Extensive experiments on a gearbox dataset validate the practicability of the proposed scheme. The results suggest that it is promising to address fault diagnosis transfer tasks in which the multiple new faults occur in the target domain, which greatly expand the application of deep transfer learning. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has become a promising tool for rotary machinery fault diagnosis, but it works well only when testing samples and training samples are independent and identically distributed. In practice, rotary machinery usually works under variable speed. The change of speed leads to the variation of samples’ distribution, which can significantly decrease the performance of the deep learning model. Scholars try to utilize transfer learning techniques for solving this problem. However, most exiting methods can just work well under target speed instead of all speed, while the target samples are always required in model training. In this article, a deep semisupervised domain generalization network (DSDGN) is proposed for rotary machinery fault diagnosis under variable speed, which can generalize the model to the fault diagnosis task under unseen speed. Under the setting of semisupervised domain generalization, only one fully labeled source (LS) domain data set and one totally unlabeled source (US) domain data set are available during training. To make full use of these data, the proposed method simultaneously utilizes Wasserstein generative adversarial network with gradient penalty (WGAN-GP)-based adversarial learning and pseudolabel-based semisupervised learning for training. The transmission and bearing fault diagnosis cases are utilized for evaluation. The comparative experiments indicate that the proposed method has a better performance than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In practical industrial applications, the working conditions of machinery are changing with long-term operation, and the health status is declining with the degradation of crucial components. When the working condition changes, prior diagnosis models cannot be generalized from one condition to another. To solve this challenging issue, in this article a robust weight-shared capsule network (WSCN) is introduced for intelligent fault diagnosis of machinery under varying working conditions. First, taking raw accelerometer signals as inputs, one-dimensional convolutional neural network is constructed to extract discriminative characteristics. Second, various capsule layers based on multistacked weight-shared capsules are developed to enhance the generalization performance for further fault classification. Finally, margin loss function as well as agreement-based dynamic routing algorithm are employed to optimize the WSCN. In this article, two diagnosis cases are carried out to demonstrate the generalization performance of the WSCN which obtains higher accuracy under varying working conditions than that of other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep transfer learning has attracted many attentions in machine intelligent fault diagnosis. However, most existed deep transfer learning algorithms encounter difficulties to detect a new emerging fault in target domain because these methods assume that the source and target domains have the same fault categories. Unfortunately, in real-world applications, new fault may emerge during machine running, which is not the same as those faults for training diagnosis models. To solve this problem, a novel fault diagnosis method named deep adversarial transfer learning network (DATLN) is proposed for new emerging fault detection. First, a one-dimension convolutional neural network is constructed to learn invariant features from the raw vibration signals of the source and target domains. Then, a multiple label classifier is trained to recognize known fault classes of the source and target domains. Finally, a decision boundary is built for the new emerging fault detection by training a classifier to recognize some target samples as new ones. Experiments on rolling bearing and gearbox demonstrate that the DATLN can implement the faults recognition with high accuracy and outperform other transfer learning methods when a new fault emerging in the target domain. [ABSTRACT FROM AUTHOR]

With the manufacturing industry stepping into the emerging new era of big data and intelligence, the amount of data collected from perception and monitoring systems with multiple smart sensors has increased tremendously. Such huge amount of multisensory data may not only power many aspects of fault diagnosis, but also bring great opportunities and challenges in modern manufacturing industry. In addition, with respect to intelligent fault diagnosis for machinery, few researches have been focused on the compound fault diagnosis under big-data circumstance. Therefore, a novel, intelligent, compound, fault decoupling method based on deep capsule network (CN) and ensemble learning is developed for compound fault decoupling and diagnosis using multisensory data. First, a decoupling CN (DCN) is constructed as the basic model. Second, taking the full advantage of multisensory data, the DCN model can be pretrained with multiple sensor data, which can obtain various pretrained DCN models. Finally, combining with ensemble learning skill, the pretrained DCN models are integrated by a combination strategy to obtain the deep ensemble CN (DECN) model for intelligent compound fault decoupling and diagnosis. The performance of the DECN model is validated on an automobile transmission (AT) data set with two compound faults, and the experimental results illustrate that the DECN model obtains higher diagnosis accuracy and decouples the compound fault correctly. [ABSTRACT FROM AUTHOR]

Background: Locomotion deficits have emerged as a novel predictor of the pathological progression of the Alzheimer's disease (AD). In this study, we hypothesize that spinal cord is impacted by the protein pathogenic in the AD. Method: To rule out the impact of the supraspinal structures, we transected spinal cord completely at thoracic level 10 to cut off all connections between the lumbosacral spinal cord and all the rostral parts of the central nervous system (CNS). The short‐term plasticity of the lumbosacral locomotor circuit was evaluated by an instrumental learning model, paw withdrawal paradigm (PaWL) and the long‐term plasticity was investigated by weekly bipedal treadmill stepping paradigm, which lasted for seven weeks. Result: Both 4mon and 13mon J20 mice showed significant decreases in the PaWL compared to their WT littermates (p = 0.0075). The spontaneous bipedal locomotion recovery was also blocked in spinal J20 mice when compared to WT littermates (p = 0.0031). These impairments of lumbar circuit plasticity were reversed by the administration of a gamma‐secretase inhibitor (Benzodiazepine‐type LY‐411575) in the spinal J20 mice, suggesting the generation of amyloid beta in local spinal cord circuit contribute to the locomotor deficits. Conclusion: The soluble beta amyloid expression levels in the lumbar spinal cord impair both the the long‐term and short‐term neuroplasticity of locomotion. [ABSTRACT FROM AUTHOR]

The peripheral nervous system may play an important role in normal bone maintenance and remodeling. Substance P (SP) is a neuropeptide associated with bone loss and formation that may mediate the effects of the nervous system. The purpose of this study is to determine if treadmill running combined with electro-acupuncture at Jiaji acupoints (Jiaji-EA) affects tibial bone mass and SP expression in rabbits with sciatic nerve injury. Twenty-four juvenile male New Zealand white rabbits were randomly assigned to one of 4 groups: sham injury control (sham), sciatic never crush control (SNCr), treadmill running (treadmill), and Jiaji-EA combined with treadmill running (ET group). The SNCr, treadmill, and ET groups all had an induced sciatic never crush injury of approximately 2mm. Control groups received no intervention; the treadmill and ET groups were trained by treadmill; the ET group also received Jiaji-EA. After the 4 weeks of treatment, toe-spreading index (TSI), BMD, bone strength, and SP expression in the tibia were significantly lower in the nerve injury groups (SNCr, treadmill, and ET) compared to the sham groups (p<0.05). Treatment (treadmill and ET groups) increased all measures compared to the SNCr group (p<0.05). Further, TSI, BMD, bone strength, and SP expression in the ET group were higher than the treadmill group (p<0.05). Our results indicate that treadmill therapy combined with electro-acupuncture at Jiaji acupoints prevents bone loss in rabbit tibias after sciatic nerve injury. This may occur in two ways: indirectly in association with axon regeneration and directly via loading on the bone mediated through increased SP expression. This study provides important evidence for the clinical treatment of bone loss after peripheral nerve injury. [ABSTRACT FROM AUTHOR]

Marital status have been found as an independent prognostic factor for survival and spousal support could provide a survival advantage in various cancer types. However, the specific effect of marital status on survival in hepatocellular carcinoma (HCC) has not been explored in detail. In this study, we used the Surveillance, Epidemiology and End Results program to identify iagnosed with HCC between 1988 and 2007. Kaplan-Meier methods and multivariable Cox regression models were used to analyze long-term cancer-specific survival (CSS) outcomes and risk factors stratified by marital status. There were significant differences among these different marital status subgroups with regard to 5-year CSS rates (P < 0.001). Married HCC patients had a better 5 year CSS rate than those unmarried patients, and widowed patients were more likely to die of their cancer. A stratified analysis showed that widowed patients always had the lowest CSS rate across different cancer stage, age and gender subgroups. Even after adjusting for known confounders, unmarried patients were at greater risk of cancer-specific mortality. Social support aimed at this population could improve the likelihood of achieving cure. [ABSTRACT FROM AUTHOR]