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Abstract Background This study explored similarities and differences among Chinese patients and rheumatologists in their attitudes towards and perceptions of traditional Chinese medicine (TCM) for Sjögren’s syndrome (SS), including analyzing factors that influenced their decision making. Methods An anonymous questionnaire was used to conduct a multicenter survey among patients with SS at three tertiary care medical centers in Beijing and among rheumatology clinicians at several hospitals across China. Results were analyzed using descriptive statistics. Results There were 942 valid questionnaires from patients from 31 provinces and cities in China, with a male-to-female ratio of approximately 1:14, a mean age of 48.81 years, and a median disease duration of 7 (4, 10) years. There were 320 valid questionnaires from rheumatologists, covering 30 provinces and cities in China, with a male-to-female ratio of approximately 0.87:1, a mean age of 48 years, and a median work duration of 10.5 (6, 15) years. The rheumatologists treated a median of 15 (11, 50) SS cases per month, and the median proportion of SS to all rheumatic diseases was 6.66% (6–10%). Many patients believed TCM could cure the root of the disease, and the most expected TCM therapies were TCM patent prescriptions and medicinal teas. Conversely, rheumatologists placed high value on the efficacy of TCM, and most commonly prescribed Chinese herbal decoctions. Most doctor-patient groups were positive about TCM treatment, citing the low side effects as the major advantage. Regression analysis showed that for patients over 40 years old with a course of disease > 4 years, the probability of using TCM has increased by 1–6 times; the probability of recommending TCM in clinical work of doctors who have worked for more than 15 years, TCM and integrated traditional Chinese and western medicine has increased 1–2 times. Conclusions TCM has become widely accepted and earned attention from doctor-patient groups, especially among older patients and experienced rheumatologists. However, negative prejudices and absence of accurate information about TCM treatments and SS itself require improvement. The contradiction between TCM dosage form and efficacy is a major problem, and patient demand for convenient and efficient TCM patent preparations suggests future work should focus on developing TCM patent preparations with clear compositions and mechanisms.

Bacterium-like particles (BLPs) have gained significant attention in vaccine development due to their potential as effective immune enhancers and antigen delivery systems. BLPs are generated by boiling lactic acid bacteria in an acidic solution and are devoid of proteins and nucleic acids, offering advantages in terms of ease of preparation, high safety, and good stability. Furthermore, by employing protein anchor (PA), heterogeneous antigens can be efficiently displayed on the surface of BLPs, resulting in enhanced delivery effectiveness. Despite these benefits, most BLP-based vaccines are currently administered via injection or intranasal delivery, with oral delivery remaining limited. This limitation is primarily due to the harsh environment of the gastrointestinal tract, which degrades the antigens displayed on the surface of these particles. To enhance the efficacy of oral immunization with subunit vaccines, we developed a simple and rapid method for self-assembling a lipid membrane onto the surface of BLPs vaccines, achieving an encapsulation efficiency of up to 99%, and the combination has good biosafety. The novel oral delivery system not only preserves the adjuvant activity of BLPs but also efficiently protects antigens from adverse gastrointestinal environments, increasing the absorption of the vaccine in intestinal Peyer’s patches (PPs). Oral immunization was required only once, and protection after the challenge was up to 100%. Furthermore, we observed rapid immunity and cross-protection. Transcriptome analysis of the small intestine suggested that immune enhancement probably be exerted by promoting the absorption and transport of antigens. Therefore, we posit that the design of this new oral delivery system presents a novel approach to advancing the development of oral subunit vaccines.

Anaerobic methane oxidation (AOM) can drive soil arsenate reduction, a process known as methane-dependent arsenate reduction (M-AsR), which is a critical driver of arsenic (As) release in soil. Low molecular weight organic acids (LMWOAs), an important component of rice root exudates, have an unclear influence and mechanism on the M-AsR process. To narrow this knowledge gap, three typical LMWOAs—citric acid, oxalic acid, and acetic acid—were selected and added to As-contaminated paddy soils, followed by the injection of 13CH4 and incubation under anaerobic conditions. The results showed that LMWOAs inhibited the M-AsR process and reduced the As(III) concentration in soil porewater by 35.1–65.7 % after 14 days of incubation. Among the LMWOAs, acetic acid exhibited the strongest inhibition, followed by oxalic and citric acid. Moreover, LMWOAs significantly altered the concentrations of ferrous iron and dissolved organic carbon in the soil porewater, consequently impacting the release of As in the soil. The results of qPCR and sequencing analysis indicated that LMWOAs inhibited the M-AsR process by simultaneously suppressing microbes associated with ANME-2d and arrA. Our findings provide a theoretical basis for modulating the M-AsR process and enhance our understanding of the biogeochemical cycling of As in paddy soils under rhizosphere conditions.

Abstract Nanomedicine-based anti-neuroinflammation strategy has become a promising dawn of Parkinson’s disease (PD) treatment. However, there are significant gaps in our understanding of the therapeutic mechanisms of antioxidant nanomedicines concerning the pathways traversing the blood-brain barrier (BBB) and subsequent inflammation mitigation. Here, we report nanozyme-integrated metal-organic frameworks with excellent antioxidant activity and chiral-dependent BBB transendocytosis as anti-neuroinflammatory agents for the treatment of PD. These chiral nanozymes are synthesized by embedding ultra-small platinum nanozymes (Ptzymes) into L-chiral and D-chiral imidazolate zeolite frameworks (Ptzyme@L-ZIF and Ptzyme@D-ZIF). Compared to Ptzyme@L-ZIF, Ptzyme@D-ZIF shows higher accumulation in the brains of male PD mouse models due to longer plasma residence time and more pathways to traverse BBB, including clathrin-mediated and caveolae-mediated endocytosis. These factors contribute to the superior therapeutic efficacy of Ptzyme@D-ZIF in reducing behavioral disorders and pathological changes. Bioinformatics and biochemical analyses suggest that Ptzyme@D-ZIF inhibits neuroinflammation-induced apoptosis and ferroptosis in damaged neurons. The research uncovers the biodistribution, metabolic variances, and therapeutic outcomes of nanozymes-integrated chiral ZIF platforms, providing possibilities for devising anti-PD drugs.

Hyperspectral images carry numerous spectral bands, and their wealth of band data is a valuable source of information for the accurate classification of ground objects. Three-dimensional (3D) convolution, although an excellent spectral information extraction method, is limited by its huge number of parameters and long model training time. To allow better integration of 3D convolution with the most popular transformer models currently available, a new architecture called mobile 3D convolutional vision transformer (MDvT) is proposed. The MDvT introduces inverted residual structure to reduce the number of model parameters and balance the data mining efficiency of low-dimensional data input. Simultaneously, a square patch is used to cut the sequence of tokens to accelerate the model operation. Through extensive experiments, we evaluated the classification overall performance of the proposed MDvT on the WHU-Hi and Pavia University datasets, and demonstrated significant improvements in classification accuracy and model runtime compared with classical deep learning models. It is worth noting that compared with directly integrating 3D convolution into the transformer model, the MDvT architecture improves the accuracy while reducing the time to train an epoch by approximately 58.54%. To facilitate the reproduction of the work in this paper, the model code is available at https://github.com/gloryofroad/MDvT.

Land surface temperature (LST), especially day-night LST difference $\lpar {{\rm LS}{\rm T}_{\rm d}-{\rm LS}{\rm T}_{\rm n}} \rpar$, is a key variable for the stability of terrestrial ecosystems, affected by vegetation and climate change. Quantifying the contribution and feedback of vegetation and climate to LST changes is critical to developing mitigation strategies. Based on LST, Normalized vegetation index (NDVI), land use (LU), air temperature (AT) and precipitation (Pre) from 2003 to 2021, partial correlation was used to analyze the response of LST to vegetation and climate. The feedback and contribution of both to LST were further quantified by using spatial linear relationships and partial derivatives analysis. The results showed that both interannual LST $\lpar {{\rm LS}{\rm T}_{\rm y}} \rpar$ and ${\rm LS}{\rm T}_{\rm d}-{\rm LS}{\rm T}_{\rm n}$ responded negatively to vegetation, and vegetation had a negative feedback effect in areas with significantly altered. Vegetation was also a major contributor to the decline of ${\rm LS}{\rm T}_{\rm d}-{\rm LS}{\rm T}_{\rm n}.$ With the advantage of positive partial correlation area of 94.99%, AT became the main driving factor and contributor to ${\rm LS}{\rm T}_{\rm y}$ change trend. Pre contributed negatively to both ${\rm LS}{\rm T}_{\rm y}$ and ${\rm LS}{\rm T}_{\rm d}-{\rm LS}{\rm T}_{\rm n}$, with contributions of −0.004 °C/y and −0.022 °C/y, respectively. AT played a decisive role in LST warming of YRB, which was partially mitigated by vegetation and Pre. The present research contributed to the detection of LST changes and improved understanding of the driving mechanism.

In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells.

Cemental tears are often misdiagnosed due to their scarcity. In this study, we reported the second largest cohort of cemental tears thus far. By reviewing the radiographic data and medical records of 63 cemental tear teeth, we found that periapical periodontitis was the most frequent diagnosis, followed by cracked tooth/root fracture and periodontitis. Most of the cemental tear teeth that did not have root canal treatment had vital pulp. The apical third of the root was the most prominent site of cemental tears. Cemental tears occurred more frequently in the palatal root of the maxillary molars and in the mesial root of the two-root mandibular molars. Uncontrollable bone loss and tooth mobility were the two main reasons for the extraction of teeth with cemental tears. We suggest that cemental tears should be included in the differential diagnosis of periapical periodontitis, cracked tooth, vertical root fracture and periodontitis, especially for teeth with periapical radiolucency and vital pulp. We believe our study could provide more insights into cemental tears, which will aid clinicians in the early diagnosis and proper treatment of cemental tears.

Bacterium-like particles (BLP) are the peptidoglycan skeleton particles of lactic acid bacteria, which have high safety, mucosal delivery efficiency, and adjuvant effect. It has been widely used in recent years in the development of vaccines. Existing anchoring proteins for BLP surfaces are few in number, so screening and characterization of new anchoring proteins are necessary. In this research, we created the OACD (C-terminal domain of Escherichia coli outer membrane protein A) to serve as an anchoring protein on the surface of BLP produced by the immunomodulatory bacteria Levilactobacillus brevis 23017. We used red fluorescent protein (RFP) to demonstrate the novel surface display system’s effectiveness, stability, and ability to be adapted to a wide range of lactic acid bacteria. Furthermore, this study employed this surface display method to develop a novel vaccine (called COB17) by using the multi-epitope antigen of Clostridium perfringens as the model antigen. The vaccine can induce more than 50% protection rate against C. perfringens type A challenge in mice immunized with a single dose and has been tested through three routes. The vaccine yields protection rates of 75% for subcutaneous, 50% for intranasal, and 75% for oral immunization. Additionally, it elicits a strong mucosal immune response, markedly increasing levels of specific IgG, high-affinity IgG, specific IgA, and SIgA antibodies. Additionally, we used protein anchors (PA) and OACD simultaneous to show several antigens on the BLP surface. The discovery of novel BLP anchoring proteins may expand the possibilities for creating mucosal immunity subunit vaccines. Additionally, it may work in concert with PA to provide concepts for the creation of multivalent or multiple vaccines that may be used in clinical practice to treat complex illnesses.

The treatment and prevention of pathogenic diseases by lactic acid bacteria (LAB) has attracted more and more attention. As a special LAB, Levilactobacillus brevis (L. brevis) has relatively less research on its antibacterial infection in vivo, and its protective effect and mechanism still need to be fully studied. In this study, we selected L. brevis 23017, which can regulate the intestinal immunity of the host animal and resist pathogen infection, to evaluate its protective role and potential molecular mechanisms in the mouse model of S. typhimurium C7731 infection. As expected, we confirmed that L. brevis 23017 reduced the diarrhea rate and increased the daily weight gain and survival rate of the mouse model, and inhibited S. typhimurium colonization in the jejunum and liver. It also reduced the level of oxidative damage and protected the integrity of intestinal tissue by increasing the activity of intestinal antioxidant enzymes (SOD, GSH-Px and T-AOC). From the perspective of intestinal mucosal barrier injury and repair, it was confirmed that L. brevis 23017 could increase the expression levels of intestinal tight junction proteins (ZO-1 and OCLN). Our research results also show that L. brevis 23017 inhibits the inflammatory response and promotes the occurrence of cellular immunity in the body by promoting the increase in IL-10 and inhibiting IL-13 in serum and intestinal tissue. Notably, L. brevis 23017 increased total secretory immunoglobulin A (SIgA) levels in the intestine, which were closely associated with elevated levels of IL-5, IL-13, pIgR, j-chain, and IgAα-chain. In addition, L. brevis 23017 increased the expression of antioxidant proteins Nrf2, NQO1, and HO-1 associated with Nrf2 signaling to inhibit intestinal oxidative damage. This mechanism may be responsible for its protective effect against S. typhimurium-infected intestine. Our study provides new evidence and theoretical support for the analysis of the anti-bacterial infection effect and mechanism of L. brevis, which will contribute to the development of L. brevis and the treatment of pathogenic bacteria intestinal infection.

Bacterium-like particles (BLPs) are hollow peptidoglycan particles obtained from food-grade Lactococcus lactis inactivated by hot acid. With the advantage of easy preparation, high safety, great stability, high loading capacity, and high mucosal delivery efficiency, BLPs can load and display proteins on the surface with the help of protein anchor (PA), making BLPs a proper delivery system. Owning to these features, BLPs are widely used in the development of adjuvants, vaccine carriers, virus/antigens purification, and enzyme immobilization. This review has attempted to gather a full understanding of the technical composition, characteristics, applications. The mechanism by which BLPs induces superior adaptive immune responses is also discussed. Besides, this review tracked the latest developments in the field of BLPs, including Lactobacillus-derived BLPs and novel anchors. Finally, the main limitations and proposed breakthrough points to further enhance the immunogenicity of BLPs vaccines were discussed, providing directions for future research. We hope that further developments in the field of antigen delivery of subunit vaccines or others will benefit from BLPs.

Abstract Aim This study aimed to explore the effect of training on organizational commitment, the mediating effect of employability and the moderating role of person–organization fit. Design A correctional cross‐sectional research design was adopted for this study. Methods A questionnaire‐based survey of 859 nurses in a public hospital in Western China was conducted to identify their perceptions of training, employability, organizational commitment and person–organization fit. Hierarchical linear regression and conditional process analysis on moderated mediation were performed. Results Training had a positive effect on organizational commitment (p

Abstract Copper (Cu) is an essential trace element in the human body that is involved in the formation of several natural enzymes, such as superoxide dismutase and cyclooxygenase. Due to the high density of the outer electron cloud of Cu, which allows the transfer of multiple electrons, Cu is often used as the catalytic center in various metabolic enzymes. However, both deficiency and excessive accumulation of Cu can result in irreversible damage to cells. Therefore, strategies to regulate Cu metabolism, such as Cu exhaustion and Cu supplementation, have emerged as attractive approaches in anticancer therapy, due to the potential damages caused by Cu metabolism disorders. Notably, recent advancements in nanotechnology have enabled the development of nanomaterials that can regulate Cu metabolism, making this therapy applicable in vivo. In this review, we provide a systematic discussion of the physical and chemical properties of Cu and summarize the applications of nanotechnology in Cu metabolism‐based antitumor therapy. Finally, we outline the future directions and challenges of nano‐Cu therapy, emphasizing the scientific problems and technical bottlenecks that need to be addressed for successful clinical translation.

Timely and accurate acquisition of crop planting areas and spatial distribution are deemed essential for grasping food configurations and guiding agricultural production. Despite the increasing research on crop mapping and changes with the development of remote sensing technology, most studies have focused on large-scale regions, with limited research being conducted in fragmented and ecologically vulnerable valley areas. To this end, this study utilized Landsat ETM+/OLI images as the data source to extract additional features, including vegetation index, terrain, and texture. We employed the Random Forest Recursive Feature Elimination (RF_RFE) algorithm for feature selection and evaluated the effectiveness of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Rotation Forest (ROF)—for crop extraction. Then, based on the optimal classifiers, the main crops in the Huangshui basin for the years of 2002, 2014, and 2022 were extracted. Finally, the transfer matrix, the gravity center model, and the Standard Deviation Ellipse (SDE) model were used to analyze the spatio—temporal changes of crops over the past 20 years in the Huangshui basin. The results showed that the spectral, vegetation index, and terrain features played a crucial role in crop extraction. Comparing the performance of the classifiers, the ROF algorithm displayed superior effectiveness in crop identification. The overall accuracy of crop extraction was above 86.97%, and the kappa coefficient was above 0.824. Notably, between 2002 and 2022, significant shifts in crop distribution within the Huangshui basin were observed. The highland barley experienced a net increase in planting area at a rate of 8.34 km2/year, while the spring wheat and oilseed rape demonstrated net decreases at rates of 16.02 km2/year and 14.28 km2/year, respectively. Furthermore, the study revealed that highland barley exhibited the most substantial movement, primarily expanding towards the southeast direction.

Mountainous vegetation type classification plays a fundamental role in resource investigation in forested areas, making it necessary to accurately identify mountain vegetation types. However, Mountainous vegetation growth is readily affected by terrain and climate, which often makes interpretation difficult. This study utilizes Sentinel-2A images and object-oriented machine learning methods to map vegetation types in the complex mountainous region of Jiuzhaigou County, China, incorporating multiple auxiliary features. The results showed that the inclusion of different features improved the accuracy of mountain vegetation type classification, with terrain features, vegetation indices, and spectral features providing significant benefits. After feature selection, the accuracy of mountain vegetation type classification was further improved. The random forest recursive feature elimination (RF_RFE) algorithm outperformed the RliefF algorithm in recognizing mountain vegetation types. Extreme learning machine (ELM), random forest (RF), rotation forest (ROF), and ROF_ELM algorithms all achieved good classification performance, with an overall accuracy greater than 84.62%. Comparing the mountain vegetation type distribution maps obtained using different classifiers, we found that classification algorithms with the same base classifier ensemble exhibited similar performance. Overall, the ROF algorithm performed the best, achieving an overall accuracy of 89.68%, an average accuracy of 88.48%, and a Kappa coefficient of 0.879.

Deep learning can extract complex and high-dimensional characteristic information with its deep structure, effectively exploring the complex relationship between landslides and their numerous influencing factors, and ultimately, more accurately predict future landslide disasters. This study builds a landslide susceptibility mapping (LSM) method based on deep learning, compares the frequency ratio (FR) sampling method with a buffer random sampling method, and performs resampling operations of landslide and non-landslide samples to explore the applicability of deep learning in LSM. In addition, six indices, precision, accuracy, recall, ROC, and the harmonic mean F1 of accuracy and recall were selected for quantitative comparison. The results show that both the resampling method proposed in this paper and the non-landslide sample selection method based on FR can significantly improve the accuracy of the model, with the area under curve (AUC) increasing by 1.34–8.82% and 3.98–7.20%, respectively, and the AUC value can be improved by 5.32–9.66% by combining the FR selection and resampling methods. Furthermore, all the deep learning models constructed in this study can obtain accurate and reliable landslide susceptibility analysis results compared to traditional models.

ObjectiveThousands of healthcare workers on the frontlines who have been battling the COVID-19 pandemic could face emotional and mental health risks even after their critical pandemic work. This study examined the impact of affective rumination on emotional exhaustion and the spillover effect of affective rumination on unhealthy food consumption among healthcare workers during recuperation.MethodsA total of 418 frontline healthcare workers from 10 Chinese medical institutions were recruited through random cluster sampling. A linear mixed model in SPSS25.0 was performed for hierarchical regression to analyze the effect of affective rumination on unhealthy food consumption via emotional exhaustion. A conditional process analysis was employed to investigate the moderating role of family support in the mediating effect of emotional exhaustion.ResultsFront-line healthcare workers scored at a medium level on an emotional exhaustion scale (2.45 ± 0.88). Affective rumination mediated by emotional exhaustion had a significant positive predictive effect on unhealthy food consumption. The indirect effect accounted for ~43.9% of the total effect. Family support amplified the effect of emotional exhaustion on unhealthy food consumption (β = 0.092, p < 0.05).ConclusionAffective rumination could be a cause of emotional exhaustion and unhealthy food consumption. First-line healthcare workers could be screened for possible emotional exhaustion through the evaluation of affective rumination in order to provide them with targeted interventions. Family support did not prove to be beneficial in all cases as it enhanced the positive effect of emotional exhaustion on unhealthy eating in the current study. Therefore, family support should be carefully integrated in future interventions.

Abstract Sepsis is a systemic inflammatory response syndrome with high morbidity and mortality mediated by infection‐caused oxidative stress. Early antioxidant intervention by removing excessively produced reactive oxygen and nitrogen species (RONS) is beneficial to the prevention and treatment of sepsis. However, traditional antioxidants have failed to improve patient outcomes due to insufficient activity and sustainability. Herein, by mimicking the electronic and structural characteristics of natural Cu‐only superoxide dismutase (SOD5), a single‐atom nanozyme (SAzyme) featuring coordinately unsaturated and atomically dispersed Cu‐N4 site was synthesized for effective sepsis treatment. The de novo‐designed Cu‐SAzyme exhibits a superior SOD‐like activity to efficiently eliminate O2•−, which is the source of multiple RONS, thus blocking the free radical chain reaction and subsequent inflammatory response in the early stage of sepsis. Moreover, the Cu‐SAzyme effectively harnessed systemic inflammation and multi‐organ injuries in sepsis animal models. These findings indicate that the developed Cu‐SAzyme possesses great potential as therapeutic nanomedicines for the treatment of sepsis.

Abstract Background With the increasing spread of COVID-19, healthcare workers, especially front-line medical staff, have become more vulnerable to emotional exhaustion. Objectives This study aimed to determine the influence of time pressure on the emotional exhaustion of front-line healthcare workers, and explore the effects of social sharing and cognitive reappraisal on this. Methods This cross-sectional study was conducted in March 2020. A total of 232 questionnaires were completed by front-line healthcare workers in Wuhan city, Hubei province, China. Hierarchical linear regression and conditional process analysis were performed to explore the relationships among time pressure, social sharing, cognitive reappraisal, and emotional exhaustion. Results Time pressure was positively associated with social sharing and emotional exhaustion. Social sharing presented the dark side, a negative effect that was always kept concealed, in terms of the impact on emotional exhaustion. Cognitive reappraisal negatively moderated the relationship between time pressure and social sharing, and it further indirectly influenced the relationship between time pressure and emotional exhaustion through social sharing. Conclusions Our findings shed light on how time pressure influences the emotional exhaustion of healthcare workers during the COVID-19 period. Although social sharing is commonly regarded as a positive behavior, we identified a dark side in terms of its impact. We also identified that improving cognitive reappraisal may present a positive strategy toward alleviating emotional exhaustion.

To date semiconductor switches are still the main enablers for electrical circuit and system reconfigurability. They however not only consume dynamic power but also dissipate static power, the former for performing on/off operation and latter for holding on/off state. These semiconductor devices are volatile and not energy efficient due to the need for holding voltage and can significantly increase the system power consumption where hundreds and thousands of switches are needed, such as in large reconfigurable intelligent surfaces and large antenna arrays. In this work, we report a non-volatile reconfigurable antenna that can switch between dual-band at 2.4 GHz and 5 GHz to a single band at 3 GHz. The measured results including reflection, gain and radiation patterns reveal promising performance, experimentally demonstrating a new approach of design and realization of RF switch integrated multi-band reconfigurable antennas. This zero-static power mechanism, along with easy fabrication on the flexible substrates would be very beneficial for Internet of Things (IoT) applications.

AimWe sought to evaluate the efficacy of acupuncture in treating the main symptoms of primary Sjögren’s syndrome, specifically dryness, pain, and fatigue.MethodsA total of 120 patients with primary Sjögren’s syndrome were randomized in a parallel-group, controlled trial. Participants received acupuncture or sham acupuncture for the first 8 weeks, then were followed for 16 weeks thereafter. The primary outcome was the proportion of participants with a ≥ 30% reduction in ≥ 2 of 3 numeric analog scale scores for dryness, pain, and fatigue. The secondary outcomes included the European League Against Rheumatism (EULAR) Sjögren’s Syndrome Patient-reported Index (ESSPRI); the EULAR Sjögren’s Syndrome Disease Activity Index; the Schirmer test score; unstimulated saliva flow; serum immunoglobulin G, A, and M concentrations; the Medical Outcome Study Short Form 36 score; salivary gland ultrasound imaging; and the Hospital Anxiety and Depression Scale score.ResultsThe proportions of patients meeting the primary endpoint were 28.33% (17/60) in the acupuncture group and 31.66% (19/60) in the sham group, without a statistically significant difference (P = 0.705). The IgG concentration at week 16 and the homogeneity in ultrasonography of the salivary glands at week 8 showed significant differences between the 2 groups (P = 0.0490 and P = 0.0334, respectively). No other differences were observed between the 2 groups. ESSPRI and unstimulated saliva flow were improved in both groups compared to baseline, albeit with a significant difference between them.ConclusionIn patients with primary Sjögren’s syndrome, acupuncture did not satisfactorily improve symptoms compared to placebo. However, interesting discoveries and possible underlying reasons were demonstrated and discussed, which may be useful to studies in the future.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT02691377].

We generated a human induced pluripotent stem cell (iPSC) line, FMUPDCi001-A, from peripheral blood mononuclear cells of a patient with mental retardation, autosomal recessive 36 (MRT36), and compound heterozygous c.219dupA and c.587C > T variants in ADAT3. This line will be a valuable resource for investigating disease mechanisms and testing gene therapies for MRT36.

The placenta plays a significant role during pregnancy. Placental dysfunction contributes to major obstetric complications, such as fetal growth restriction and preeclampsia. Currently, there is no effective treatment for placental dysfunction in the perinatal period, and prophylaxis is often delivered too late, at which point the disease manifestation cannot be prevented. However, with recent integration of nanoscience and medicine to perform elaborate experiments on the human placenta, it is expected that novel and efficient nanotherapies will be developed to resolve the challenge of managing placental dysfunction. The advent of nanomedicine has enabled the safe and targeted delivery of drugs using nanoparticles. These smart nanoparticles can load the necessary therapeutic substances that specifically target the placenta, such as drugs, targeting molecules, and ligands. Packaging multifunctional molecules into specific delivery systems with high targeting ability, diagnosis, and treatment has emerged as a novel theragnostic (both therapeutic and diagnostic) approach. In this review, the authors discuss recent advances in nanotechnology for placental dysfunction treatment. In particular, the authors highlight potential candidate nanoparticle-loaded molecules that target the placenta to improve utero-placental blood flow, and reduce reactive oxygen species and oxidative stress. The authors intend to provide basic insight and understanding of placental dysfunction, potential delivery targets, and recent research on placenta-targeted nanoparticle delivery systems for the potential treatment of placental dysfunction. The authors hope that this review will sensitize the reader for continued exploration of novel nanomedicines.

Primary Sjögren’s syndrome (pSS) is a chronic, systemic autoimmune disease defined by exocrine gland hypofunction resulting in dry eyes and dry mouth. Despite increasing interest in biological therapies for pSS, achieving FDA-approval has been challenging due to numerous complications in the trials. The current literature lacks insight into a molecular-target-based approach to the development of biological therapies. This review focuses on novel research in newly defined drug targets and the latest clinical trials for pSS treatment. A literature search was conducted on ClinicalTrials.gov using the search term “Primary Sjögren’s syndrome”. Articles published in English between 2000 and 2021 were included. Our findings revealed potential targets for pSS treatment in clinical trials and the most recent advances in understanding the molecular mechanisms underlying the pathogenesis of pSS. A prominent gap in current trials is in overlooking the treatment of extraglandular symptoms such as fatigue, depression, and anxiety, which are present in most patients with pSS. Based on dryness and these symptom-directed therapies, emerging biological agents targeting inflammatory cytokines, signal pathways, and immune reaction have been studied and their efficacy and safety have been proven. Novel therapies may complement existing non-pharmacological methods of alleviating symptoms of pSS. Better grading systems that add extraglandular symptoms to gauge disease activity and severity should be created. The future of pSS therapies may lie in gene, stem-cell, and tissue-engineering therapies.

Preeclampsia: when cells aren’t invasive enough Cancer-associated proteins play a role in preeclampsia, a potentially life-threatening disorder of pregnancy marked by high blood pressure and protein in the urine. The causes of preeclampsia are poorly understood, but the tissue that nourishes the fetus, the placenta, is known to be involved. Knowing that for healthy placenta formation, cells called trophoblasts must show cancer cell-like behavior and invade the developing tissue, Li-Ping Jin and Kai Wang at Tongji University School of Medicine in Shanghai, China, and co-workers investigated the role of cancer-associated LOX proteins, previously linked to cell invasiveness. Preeclamptic placentas showed low LOX levels, poor trophoblast invasion, and excessive formation of collagen, an important connective tissue. Further analysis showed that this excessive collagen is broken down, becoming the diagnostic urinary protein. These results illuminate potential markers for early diagnosis and treatment of preeclampsia.

Efficient and accurate vegetation type extraction from remote sensing images can provide decision makers with basic forest cover and land use information, and provides a reliable basis for long-term monitoring. With the development of deep learning, the convolutional neural network (CNN) has been used successfully to classify tree species in many studies, but CNN models have rarely been applied in the classification of vegetation types on larger scales. To evaluate the performance of CNN models in the classification of vegetation types, this paper compared the classification accuracy of nine dominant land cover types in Baishuijiang National Nature Reserve with four models: 3D-CNN, 2D-CNN, JSSAN (joint spatial–spectral attention network) and Resnet18, using sentinel-2A data. Comparing the difference in classification accuracy between the direct use of raw sentinel images and fused feature indices sentinel images, the results showed that adding feature indices can improve the overall accuracy of the model. After fusing the characteristic bands, the accuracy of the four models was improved significantly, by 5.46–19.33%. The best performing 3D-CNN model achieved the highest classification accuracy with an overall accuracy of 95.82% and a kappa coefficient of 95.07%. In comparison, 2D-CNN achieved an overall accuracy of 79.07% and a kappa coefficient of 75.44%, JSSAN achieved an overall accuracy of 81.67% and a kappa coefficient of 78.56%, and Resnet18 achieved an overall accuracy of 93.61% and a kappa coefficient of 92.45%. The results showed that the 3D-CNN model can effectively capture vegetation type cover changes from broad-leaved forests at lower elevation, to shrublands and grasslands at higher elevation, across a range spanning 542–4007 m. In experiments using a small amount of sample data, 3D-CNN can better incorporate spatial–spectral information and is more effective in distinguishing the performance of spectrally similar vegetation types, providing an efficient and novel approach to classifying vegetation types in nature reserves with complex conditions.

The purpose of the study was to use exome sequencing (ES) to study the contribution of single-gene disorders to recurrent non-immune hydrops fetalis (NIHF) and retrospectively evaluate the value of genetic diagnosis on prenatal management and pregnancy outcome. From January 2012 to October 2018, a cohort of 28 fetuses with recurrent NIHF was analyzed by trio ES. Fetuses with immune hydrops, non-genetic factors (including infection, etc.), karyotype, or CNV abnormalities were excluded. Variants were interpreted based on ACMG/AMP guidelines. Fetal therapy was performed on seven fetuses. Of the 28 fetuses, 10 (36%) were found to carry causal genetic variants (pathogenic or likely pathogenic) in eight genes (GBA, GUSB, GBE1, RAPSN, FOXC2, PIEZO1, LZTR1, and FOXP3). Five (18%) fetuses had variant(s) of uncertain significance (VUS). Of the 10 fetuses with definitive molecular diagnosis, five (50%) were diagnosed with inborn errors of metabolism. Among the seven fetuses who received fetal therapy, two had definitive molecular diagnosis and resulted in neonatal death. Among the remaining five fetuses with negative results, four had newborn survival and one had intrauterine fetal death. Trio ES could facilitate genetic diagnosis of recurrent NIHF and improve the prenatal management and pregnancy outcome.

Two widely used approaches, nonparametric approach and Budyko framework approach, were used to calculate precipitation elasticity of streamflow (ε) in China’s main river basins. While the Budyko framework illustrates only the effect of climate on ε, the nonparametric approach illustrates the effects of both climate and human activity on ε. Both approaches showed similar spatial pattern of ε, with high values for northern arid catchments and low values for southern humid catchments, suggesting high sensitivity of streamflow to climate in the arid catchments in China’s north. Inland catchments had low ε values, probably because most of the annual streamflow was driven by glacial and snowmelt. While strong anthropologic activities reduce the sensitivity of streamflow to precipitation in some northern arid catchments, which was indicated by lower ε values produced by nonparametric approach, the combined use of the two approaches underscored the significance in identifying the effects of anthropologic factors on streamflow.

Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1) modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2) parameterizing hydrological models in ungauged catchments, (3) improving hydrological model structure, and (4) using new remote sensing precipitation data.

Hydrological models with the conceptual tipping bucket and the process-based evapotranspiration models are the most common tools in hydrology. However, these models consistently fail to replicate long-term and slow dynamics of a hydrological system, indicating the need for model augmentation and shift in approach. This study employed an entirely different approach - system dynamics - towards more realistic replication of long-term and slow hydrological behaviors by removing limits of exogenous climate on evapotranspiration and involving endogenous soil water-vegetation feedback loop. Using the headwaters of Baiyang Lake in China as a case study, the mechanisms of slow hydrological dynamics were gradually unraveled from 1982 to 2015 through wavelet analysis, Granger's causality test, and system dynamics. The wavelet analysis and Granger's causality test identified a negative-correlated, bidirectional causal relationship between evapotranspiration and the water budget across distinct climatic periodicities, suggesting a robust endogenous soil water-vegetation feedback structure operating on a long-term scale. The system dynamics approach successfully captured the slow behavior of the hydrological system under both natural and human-intervention scenarios, demonstrating a self-sustained oscillation arising within the system's boundary. Conventional hydrological models, which rely on process-based evapotranspiration models, operate on an instantaneous scale and are thus susceptible to short-time climatic and vegetation physiological variations. This results in inaccurate depletion rate of soil water stock and in turn, can lead to incorrect calculations of other hydrological variables. However, long-term and slow hydrological dynamics typically involves in endogenous state-dependent modulation and feedback related to changes in vegetation structure, thus are insensitive to exogenous disturbances and can be well replicated using system dynamics approach. This insight that the failure of hydrological models to replicate slow dynamics can be attributed to a time-scale mismatch may offer potential solutions for improving conventional hydrological models. [ABSTRACT FROM AUTHOR]