Translation and validation of the Chinese self-report version of Spinal Cord Independence Measure (SCIM-SR): Rasch psychometric analysis and online application.

Spinal Cord Independence Measure (SCIM) was an important functional outcome measure specifically designed for spinal cord injury (SCI) patients, with the self-reported version of SCIM (SCIM-SR) published in 2013. This study aims to translate the SCIM-SR into Chinese, and to investigate the validity of Chinese SCIM-SR among SCI patients. This Chinese version of SCIM-SR was translated into Chinese in a standardized approach, and then filled out by a sample of patients with SCI (n = 205) within 3 days after admission. Validity of Chinese SCIM-SR was then analyzed using Rasch analysis and principal component analysis. The subscale Selfcare and subscale Mobility showed good fit to the Rasch model, with no significance found in Chi-square test results for item-trait interaction, using Bonferroni adjustment for the significant level (χ2 =18.125, P = 0.111; χ2 =33.629, P = 0.006). Mean fit residual for items and persons of each subscale were within ± 2.5. The model fit of the subscale of Respiration and Sphincter Management was not satisfactory even after deleting one item and merging two items with local dependence. However, Kaiser-Meyer-Olkin test was > 0.50 in total score and all the subscales of Chinese SCIM-SR, and P < 0.05 in the Bartlett's test. There was no differential item functioning for gender, time post injury, age, and etiology in any of the three subscales. An online version of Chinese SCIM-SR was also developed. It is concluded that the SCIM-SR in Chinese is valid for application in individuals with SCI. SCIM-SR is considered as an important tool for self-reporting functional status from SCI individuals' perspective.

Research progress on the pharmacological activity, biosynthetic pathways, and biosynthesis of crocins.

Crocins are water-soluble apocarotenoids isolated from the flowers of crocus and gardenia. They exhibit various pharmacological effects, including neuroprotection, anti-inflammatory properties, hepatorenal protection, and anticancer activity. They are often used as coloring and seasoning agents. Due to the limited content of crocins in plants and the high cost of chemical synthesis, the supply of crocins is insufficient to meet current demand. The biosynthetic pathways for crocins have been elucidated to date, which allows the heterologous production of these valuable compounds in microorganisms by fermentation. This review article provides a comprehensive overview of the chemistry, pharmacological activity, biosynthetic pathways, and heterologous production of crocins, aiming to lay the foundation for the large-scale production of these valuable natural products by using engineered microbial cell factories.

Infant Formulas With Partially or Extensively Hydrolyzed Milk Proteins for the Prevention of Allergic Diseases: A Systematic Review and Meta-Analysis of Clinical Trials.

Despite the widely recommended usage of partially hydrolyzed formula (PHF) or extensively hydrolyzed formula (EHF) of milk protein for preventing allergic diseases (ADs), clinical studies have been inconclusive regarding their efficacy compared with that of cow's milk formula (CMF) or breast milk (BM). We aimed to systematically evaluate the effects of PHF or EHF compared with those of CMF or BM on risk of ADs (cow's milk allergy, allergic rhinitis, eczema, asthma, wheeze, food allergy, and sensitization) in children. We searched PubMed, Embase, Cochrane Library, and Web of Science for clinical trials published from inception to 21 October, 2022. We used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to grade the strength of evidence. Overall, 24 trials (10,950 infants) were included, 17 of which specifically included high-risk infants. GRADE was low for the evidence that, compared with CMF, infants early fed with EHF had lower risk of cow's milk allergy at age 0-2 y [relative risk (RR): 0.62; 95% CI: 0.39, 0.99]. Moderate evidence supported that PHF and EHF reduced risk of eczema in children aged younger or older than 2 y, respectively (RR: 0.71; 95% CI: 0.52, 0.96; and RR: 0.79; 95% CI: 0.67, 0.94, respectively). We also identified moderate systematic evidence indicating that PHF reduced risk of wheeze at age 0-2 y compared with CMF (RR: 0.50; 95% CI: 0.29, 0.85), but PHF and EHF increased the risk compared with BM (RR: 1.61; 95% CI: 1.11, 2.31; and RR: 1.64; 95% CI: 1.26, 2.14). Neither PHF nor EHF had significant effects on other ADs in children of any age. In conclusion, compared with CMF, PHF, or EHF had different preventive effect on cow's milk allergy, eczema, and wheeze. Compared with BM, both PHF and EHF may increase risk of wheeze but not other ADs. Given that most trials included only high-risk infants, more research on non-high-risk infants is warranted before any generalization is attempted. This protocol was registered at PROSPERO as CRD42022320787.

Comparative efficacy and safety of JAK inhibitors in the treatment of moderate-to-severe alopecia areata: a systematic review and network meta-analysis.

We performed a Bayesian network meta-analysis to indirectly compare the relative efficacy and safety of the latest JAK inhibitors for moderate-to-severe alopecia areata (AA). 13 trials totaling 3,613 patients were included. Two low-dose groups of oral formulations (ritlecitinib 10mg and ivarmacitinib 2mg) and two topical formulations (delgocitinib ointment and ruxolitinib cream) appeared to be relatively ineffective against moderate-to-severe AA. Ranking analysis suggested that brepocitinib 30mg has the best relative effect in reducing the SALT score (sucra = 0.9831), and demonstrated comparable efficacy to deuruxolitinib 12mg (sucra = 0.9245), followed by deuruxolitinib 8mg (sucra = 0.7736). Regarding the SALT50 response, brepocitinib 30mg ranked highest (sucra = 0.9567), followed by ritlecitinib 50mg (sucra = 0.8689) and deuruxolitinib 12mg (sucra = 0.7690). For achieving the SALT75 response, deuruxolitinib 12mg had the highest probability (sucra = 0.9761), followed by deuruxolitinib 8mg (sucra = 0.8678) and brepocitinib 30mg (sucra = 0.8448). Deuruxolitinib 12mg might be the most effective therapy for patients with severe AA (sucra = 0.9395), followed by ritlecitinib 50mg (sucra = 0.8753) and deuruxolitinib 8mg (sucra = 0.8070). Deuruxolitinib 12mg/8mg demonstrated notable efficacy for moderate-to-severe AA, and is expected to be a new treatment option for AA. It was worth noting that deuruxolitinib exhibit a greater likelihood of causing adverse events in comparison to other JAK inhibitors. Ritlecitinib 50mg seemed to exhibit fewer adverse effects in the high-dose groups of oral JAK inhibitors and might be an optimal choice to balance safety and efficacy. The majority of JAK inhibitors exhibited acceptable short-term safety profiles. To enhance the applicability and accuracy of our research, further head-to-head trials with longer follow-up periods are needed. Systematic Review Registration: identifier [CRD42022368012].

Building and Validation of an Acute Event Prediction Model for Severe Mental Disorders.

Background: The global incidence of acute events in psychiatric patients is intensifying, and models to successfully predict acute events have attracted much attention. Objective: To explore the influence factors of acute incident severe mental disorders (SMDs) and the application of Rstudio statistical software, and build and verify a nomogram prediction model. Methods: SMDs were taken as research objects. The questionnaire survey method was adopted to collect data. Patients with acute event independent factors were screened. R software multivariable Logistic regression model was constructed and a nomogram was drawn. Results: A total of 342 patients with SMDs were hospitalized, and the number of patients who encountered acute events was 64, which accounted for 18.70% of all patients. Statistical significances were found in many aspects (all P ˂ 0.05). Such aspects included Medication adherence, disease diagnosis, marital status, caregivers, social support and the hospitalization environment (odds ratio (OR) = 4.08, 11.62, 12.06, 10.52, 0.04 and 0.61, respectively) were independent risk factors for the acute events of patients with SMDs. The prediction model was modeled, and the AUC was 0.77 and 0.80. The calibration curve shows that the model has good calibration. The clinical decision curve shows that the model has a good clinical effect. Conclusion: The constructed risk prediction model shows good prediction effectiveness in the acute events of patients with SMDs, which is helpful for the early detection of clinical mental health staff at high risk of acute events.

Producing high-quantity and high-quality recombinant adeno-associated virus by low-cis triple transfection.

Recombinant adeno-associated virus (rAAV)-based gene therapy is entering clinical and commercial stages at an unprecedented pace. Triple transfection of HEK293 cells is currently the most widely used platform for rAAV manufacturing. Here, we develop low-cis triple transfection that decreases transgene plasmid use by 10- to 100-fold and overcomes several major limitations associated with standard triple transfection. This new method improves packaging of yield-inhibiting transgenes by up to 10-fold, and generates rAAV batches with reduced plasmid backbone contamination that otherwise cannot be eliminated in downstream processing. When tested in mice and compared with rAAV produced by standard triple transfection, low-cis rAAV shows comparable or superior potency and results in diminished plasmid backbone DNA and RNA persistence in tissue. Mechanistically, low-cis triple transfection relies on the extensive replication of transgene cassette (i.e., inverted terminal repeat-flanked vector DNA) in HEK293 cells during production phase. This cost-effective method can be easily implemented and is widely applicable to producing rAAV of high quantity, purity, and potency.

Ethical and regulatory challenges of large language models in medicine.

With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs. A comprehensive framework and mitigating strategies will be imperative for the responsible integration of LLMs into medical practice, ensuring alignment with ethical principles and safeguarding against potential societal risks.

A nomogram to predict the risk of acute ischemic stroke in patients with maintenance hemodialysis: a retrospective cohort study.

OBJECTIVE: Acute ischemic stroke (AIS) stands as a leading cause of death and disability globally. This study aimed to investigate the risk factors linked with AIS in patients undergoing maintenance hemodialysis (MHD) and to create and validate nomogram models. METHODS: We examined the medical records of 314 patients with stage 5 chronic kidney disease (CKD5) undergoing MHD, who sought neurology outpatient department consultation for suspected AIS symptoms between January 2018 and December 2023. These 314 patients were randomly divided into the training cohort (n=222) and validation cohort (n=92). The Least Absolute Shrinkage Selection Operator (LASSO) regression model was employed for optimal feature selection in the AIS risk model. Subsequently, multivariable logistic regression analysis was used to construct a predictive model incorporating the features selected through LASSO. This predictive model's performance was assessed using the C-index and the area under the receiver operating characteristic curve (AUC). Additionally, calibration and clinical utility were evaluated through calibration plots and decision curve analysis (DCA). The model's internal validation was conducted using the validation cohort. Resaults: Predictors integrated into the prediction nomogram encompassed cardiovascular disease (CVD) (Odds Ratio [OR] 7.95, 95% confidence interval [CI] 2.400-29.979), smoking (OR 5.7, 95% CI 1.661-21.955), dialysis time (OR 5.91, 95% CI 5.866-29.979), low-density lipoprotein (LDL) (OR 2.99, 95% CI 0.751-13.007), and fibrin degradation products (FDP) (OR 5.47, 95% CI 1.563-23.162). The model exhibited robust discrimination, with a C-index of 0.877 and 0.915 in the internal training and validation cohorts, respectively. The AUC for the training set was 0.857, and a similar AUC of 0.905 was achieved in the validation cohort. Decision curve analysis (DCA) demonstrated a positive net benefit within a threshold risk range of 2 to 96%. CONCLUSION: The proposed nomogram effectively identifies MHD patients at high risk of AIS at an early stage. This model holds the potential to aid clinicians in making preventive recommendations.

Competitive-like binding between carbon black and CTNNB1 to ΔNp63 interpreting the abnormal respiratory epithelial repair after injury.

Airway epithelium is extraordinary vulnerable to damage owning to continuous environment exposure. Subsequent repair is therefore essential to restore the homeostasis of respiratory system. Disruptions in respiratory epithelial repair caused by nanoparticles exposure have been linked to various human diseases, yet implications in repair process remain incompletely elucidated. This study aims to elucidate the key stage in epithelial repair disturbed by carbon black (CB) nanoparticles, highlighting the pivotal role of ΔNp63 in mediating the epithelium repair. A competitive-like binding between CB and beta-catenin 1 (CTNNB1) to ΔNp63 is proposed to elaborate the underlying toxicity mechanism. Specifically, CB exhibits a remarkable inhibitory effect on cell proliferation, leading to aberrant airway epithelial repair, as validated in air-liquid culture. ΔNp63 drives efficient epithelial proliferation during CB exposure, and CTNNB1 was identified as a target of ΔNp63 by bioinformatics analysis. Further molecular dynamics simulation reveals that oxygen-containing functional groups on CB disrupt the native interaction of CTNNB1 with ΔNp63 through competitive-like binding pattern. This process modulates CTNNB1 expression, ultimately restraining proliferation during respiratory epithelial repair. Overall, the current study elucidates that the diminished interaction between CTNNB1 and ΔNp63 impedes respiratory epithelial repair in response to CB exposure, thereby enriching the public health risk assessment on CB-related respiratory diseases.

BRD4L cooperates with MYC to block local tumor invasion via suppression of S100A10.

Targeted therapy based on BRD4 and MYC shows promise due to their well-researched oncogenic functions in cancer, but their tumor-suppressive roles are less understood. In this study, we employ a systematic approach to delete exons that encode the low-complexity domain (LCD) of BRD4L in cells by using CRISPR-Cas9. In particular, the deletion of exon 14 (BRD4-E14) results in cellular morphological changes towards spindle-shaped and loosely packed. BRD4-E14 deficient cells show increased cell migration and reduced cell adhesion. The expression of S100A10 was significantly increased in cells lacking E14. BRD4L binds with MYC via the E14-encoded region of the LCD to inhibit the expression of S100A10. In cancer tissues, there is a positive correlation between BRD4 and MYC, while both of these proteins are negatively associated with S100A10 expression. Finally, knocking out the BRD4-E14 region or MYC promotes tumor growth in vivo. Together, these data support a tumor-suppressive role of BRD4L and MYC in some contexts. This discovery emphasizes the significance of a discreetly design and precise patient recruitment in clinical trials that testing cancer therapy based BRD4 and MYC.

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study.

BACKGROUND: Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies. OBJECTIVE: This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs). METHODS: We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families. RESULTS: A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope. CONCLUSIONS: This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

Boryl Radical as a Catalyst in Enabling Intra- and Intermolecular Cascade Radical Cyclization Reactions: Construction of Polycyclic Molecules.

Cascade radical cyclization constitutes an atom- and step-economic route for rapid assembly of polycyclic molecular skeletons. Although an array of redox-active metal catalysts has recently shown robust applications in enabling various catalytic cascade radical processes, the use of free organic radical as the catalyst, which is capable of triggering strategically distinct cascades, has rarely been developed. Here, we disclosed that the benzimidazolium-based N-heterocyclic carbene (NHC)-boryl radical is capable of catalyzing cascade cyclization reactions in both intra- and intermolecular pathways, assembling [5,5] fused bicyclic and [6,6,6] fused tricyclic molecules, respectively. The catalytic reactions start with the chemo- and regioselective addition of the boryl radical catalyst to a tethered alkene or alkyne moiety, followed by either an intramolecular formal [3+2] or an intermolecular [2+2+2] cycloaddition process to construct bicyclo[3.3.0]octane or tetrahydrophenanthridine skeletons, respectively. Eventually, a ß-elimination occurs to release the boryl radical catalyst, completing a catalytic cycle. High to excellent diastereoselectivity is achieved in both catalytic reactions under substrate control.

Neural Q-learning for discrete-time nonlinear zero-sum games with adjustable convergence rate.

In this paper, an adjustable Q-learning scheme is developed to solve the discrete-time nonlinear zero-sum game problem, which can accelerate the convergence rate of the iterative Q-function sequence. First, the monotonicity and convergence of the iterative Q-function sequence are analyzed under some conditions. Moreover, by employing neural networks, the model-free tracking control problem can be overcome for zero-sum games. Second, two practical algorithms are designed to guarantee the convergence with accelerated learning. In one algorithm, an adjustable acceleration phase is added to the iteration process of Q-learning, which can be adaptively terminated with convergence guarantee. In another algorithm, a novel acceleration function is developed, which can adjust the relaxation factor to ensure the convergence. Finally, through a simulation example with the practical physical background, the fantastic performance of the developed algorithm is demonstrated with neural networks.

The effectiveness of virtual reality technology in student nurse education: A systematic review and meta-analysis.

AIM: The purpose of this study was to analyze the effectiveness of virtual reality technology in nursing education. BACKGROUND: Virtual reality technology is regarded as one of the advanced and significant instructional tools in contemporary education. However, its effectiveness in nursing education remains a subject of debate, and there is currently limited comprehensive research discussing the impact of varying degrees of virtual technology on the educational effectiveness of nursing students. DESIGN: Systematic review and meta-analysis. METHODS: The present systematic review and meta-analysis were applied according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement. The PubMed, Embase, CINAHL, ProQuest, Cochrane Library, Web of Science, and Scopus were searched for relevant articles in the English language. The methodologies of the studies evaluated were assessed using Cochrane Risk of Bias2 (ROB 2) tool and Joanna Briggs Institute (JBI) assessment tool. We took the learning satisfaction, knowledge, and skill performance of nursing students as the primary outcomes, and nursing students' self-efficacy, learning motivation, cognitive load, clinical reasoning, and communication ability were assessment as secondary outcomes. The meta-analysis was performed using R 4.3.2 software according to PRISMA guidelines. Heterogeneity was assessed by I2 and P statistics. Standardized mean difference (SMD) and 95 % confidence intervals (CIs) were used as effective indicators. RESULTS: Twenty-six studies were reviewed, which involved 1815 nursing students. The results showed that virtual reality teaching, especially immersive virtual reality, was effective in improving nursing students' learning satisfaction (SMD: 0.82, 95%CI: 0.53-1.11, P < 0.001), knowledge (SMD: 0.56, 95%CI: 0.34-0.77, P < 0.001), skill performance (SMD: 1.13, 95 % CI: 0.68-1.57, P < 0.001), and self-efficacy (SMD: 0.64, 95%CI: 0.21,1.07, P < 0.001) compared to traditional teaching methods. However, the effects of virtual reality technology on nursing students' motivation, cognitive load, clinical reasoning, and communication ability were not significant and require further research. CONCLUSIONS: The results of this study show that virtual reality technology has a positive impact on nursing students. Nonetheless, it is crucial not to underestimate the effectiveness of traditional education methods, and future research could analyze the impact of different populations on nursing education while improving virtual reality technology, to more comprehensively explore how to improve the quality of nursing education. Moreover, it is imperative to emphasize the integration of virtual education interventions with real-world experiences promptly. This integration is essential for bridging the gap between the virtual learning environment and real-life scenarios effectively. REGISTRATION NUMBER: CRD42023420497 (https://www.crd.york.ac.uk/PROSPERO/#recordDetails).

Lanthanide-based nanomaterials for temperature sensing in the near-infrared spectral region: illuminating progress and challenges.

Being first proposed as a method to overcome limitations associated with conventional contact thermometers, luminescence thermometry has been extensively studied over the past two decades as a sensitive and fast approach to remote and minimally invasive thermal sensing. Herein, lanthanide (Ln)-doped nanoparticles (Ln-NPs) have been identified as particularly promising candidates, given their outstanding optical properties. Known primarily for their upconversion emission, Ln-NPs have also been recognized for their ability to be excited with and emit in the near-infrared (NIR) regions matching the NIR transparency windows. This sparked the emergence of the development of NIR-NIR Ln-NPs for a wide range of temperature-sensing applications. The shift to longer excitation and emission wavelengths resulted in increased efforts being put into developing nanothermometers for biomedical applications, however most research is still preclinical. This mini-review outlines and addresses the challenges that limit the reliability and implementation of luminescent nanothermometers to real-life applications. Through a critical look into the recent developments from the past 4 years, we highlight attempts to overcome some of the limitations associated with excitation wavelength, thermal sensitivity, calibration, as well as light-matter interactions. Strategies range from use of longer excitation wavelengths, brighter emitters through strategic core/multi-shell architectures, exploitation of host phonons, and a shift from double- to single-band ratiometric as well as lifetime-based approaches to innovative methods based on computation and machine learning. To conclude, we offer a perspective on remaining gaps and where efforts should be focused towards more robust nanothermometers allowing a shift to real-life, e.g., in vivo, applications.

Defoliation, trampling and nutrient return differentially influence grassland productivity by modulating trait-dependent plant community composition: insights from a simulated grazing experiment.

Ungulate grazing involves multiple components, including defoliation, dung and urine return, and trampling, which supply offsetting or synergistic effects on plant community composition and productivity (ANPP), but these effects have not been fully studied. Plant functional traits may reflect the response of plants to disturbance and their impact on ecosystem functions. Species turnover and intraspecific trait variation (ITV) are important drivers of community trait composition. We conducted a simulated grazing experiment in a steppe grassland in northern China to examine the effects of defoliation, dung and urine return, and trampling on community-weighted mean (CWM), functional diversity (FD) and ANPP, and to disentangle the roles of species turnover and ITV in driving these changes. We found that defoliation had a dominant effect on CWMs and FDs of all four traits through species turnover and ITV, respectively, resulting in a convergence of traits towards as more resource-acquisitive strategy. Dung-urine return resulted in more resource-acquisitive community traits mainly through ITV, whereas there were no significant effects on FDs except for leaf C/N. Trampling increased CWM of leaf dry matter content primarily driven by ITV, and had no significant effect on FDs. Furthermore, our simulated grazing positively affected ANPP, primarily due to nutrient additions from dung and urine, and ITV largely explained the variation in ANPP. These findings highlight the multifaceted effects of grazing components on community structure and ANPP, and the significance of ITV in shaping grassland plant communities and productivity.

Oriented feature pyramid network for small and dense wheat heads detection and counting.

Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.

Efficacy and safety of venlafaxine hydrochloride combined with tandospirone citrate for patients with vascular depression accompanied by somatic symptoms: An open-labeled randomized control trial.

AIMS: To explore the pharmacological treatment of vascular depression (VaDep) and whether the blood levels of neurotransmitters can reflect the VaDep severity. METHODS: VaDep patients with somatic symptoms were enrolled and randomly received venlafaxine + tandospirone (Combined Group) or venlafaxine (Monotherapy Group). The treatment efficacy was assessed by Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Patient Health Questionnaire-15 (PHQ-15). The levels of blood monoamine neurotransmitters were measured by enzyme-linked immunosorbent assay. RESULTS: Both groups reported a progressive decrease in HAMD, HAMA, and PHQ-15 scores to below the baseline after the respective treatment. Compared with the Monotherapy Group, the Combined Group reported a significant decrease in HAMD score at week 2 and markedly lower HAMA and PHQ-15 scores at weeks 1, 2, 4, and 8. Both groups showed a decrease in the levels of blood monoamine neurotransmitters at weeks 4 and 8 when compared with the baseline. A strong positive association was evident between the plasma 5-HT levels and the HAMD score. CONCLUSION: The combined therapy rapidly acts on VaDep comorbid with anxiety and somatic symptoms and significantly alleviates the anxiety and somatic symptoms. The plasma levels of 5-HT may serve as potential objective candidates in evaluating VaDep severity and the efficacy of the undertaken treatment regimen.

Submarine groundwater discharge and ocean acidification: Implications from China's coastal waters.

Ocean acidification (OA) is a global environmental concern, and submarine groundwater discharge (SGD) is a potentially process that enhances OA. This review summarizes the relationship between two types of constituents carried by SGD into China's seawater and OA. 1) Current research predominantly concentrates on constituent fluxes from SGD, neglecting its ecological impacts on carbon and nutrients budgets, as well as the mechanisms between carbon and nutrients. 2) Uncertainties persist in SGD research methods and acidification characterization. 3) There's a need to enhance quantitative research methods of SGD-OA, particularly in areas with intricate biogeochemical processes. Effective identification methods are crucial to quantify SGD's contribution to OA. Investigating core scientific questions, including SGD's impact on OA rates and scales, is paramount. While the primary focus is on SGD-OA research in China, insights gained from novel perspectives could have broader value for coastal management globally.