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Cleavage of MALAT1 RNA by 14-nt sgRNA-guided tRNase ZL

We have been developing a gene suppression technology, tRNase Z L -utilizing efficacious (TRUE) gene silencing, in which artificially designed small guide RNA (sgRNA) guides tRNase Z L to cleave cellular target RNA. In this study, we examined 14-nt linear-type sgRNAs, which are fully 2′- O -methylated and have full phosphorothioate linkages, for their ability to suppress a level of a nuclear-localized long non-coding RNA, Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1). The MALAT1 RNA is implied to be involved in stress responses and diseases including cancers. Specifically, we designed six 14-nt linear-type sgRNAs, sgRM1 − sgRM6 that target the human MALAT1 RNA. sgRM1, sgRM2 and sgRM6 suppressed the MALAT1 RNA level, while the other sgRNAs showed little effect. In order to demonstrate that the suppression effect of sgRM1, sgRM2 and sgRM6 on the MALAT1 RNA level is caused by TRUE gene silencing, we performed in vitro tRNase Z L cleavage assay, microscopic analysis for nuclear existence of sgRNA, and tRNase Z L knockdown experiment. For the in vitro tRNase Z L cleavage assay, three 30-nt MALAT1 RNA fragments, TM1, TM2 and TM6 were prepared, which were RNA targets for sgRM1, sgRM2 and sgRM6, respectively. All of the sgRNAs guided recombinant tRNase Z L in vitro to cleave their own targets, although the cleavage efficiency changed depending on target/sgRNA pairs. By fluorescence microscopy, a 14-nt 5′-Alexa568-labeled sgRNA released from liposome was observed to be distributed ubiquitously in A549 cells with higher density in the nucleus, where both the target MALAT1 RNA and tRNase Z L exist. Knockdown of tRNase Z L by siRNA attenuated the suppression effect of sgRM1, sgRM2 and sgRM6 on the MALAT1 RNA level. We also demonstrated that the effective sgRNAs sgRM1, sgRM2 and sgRM6 reduce A549 cell viability.

Lactic acid regulates antitumor immunity in canine invasive urothelial carcinoma

Canine invasive urothelial carcinoma (iUC) is a fatal malignant neoplasm that closely resembles human muscle-invasive bladder cancer in terms of histopathological features, molecular alterations, and clinical behavior. These similarities suggest that canine iUC represents a valuable spontaneous model for studying human bladder cancer. Tumor microenvironment (TME) plays a crucial role in tumor progression. Tumor-derived lactic acid has been implicated in the suppression of antitumor immunity and the promotion of tumor growth by altering the metabolic status of immune cells within the TME. However, the interaction between tumor metabolism and immune cells in the TME remains unclear in dogs. This study reanalyzed previously reported RNA-seq data to investigate the mechanisms underlying enhanced glycolysis in canine iUC. ERBB2 overexpression was found to induce AKT phosphorylation and increase extracellular lactic acid levels in vitro, activating the ERBB2-AKT-glycolysis axis and upregulating monocarboxylate transporter 4 (MCT4). MCT4 knockdown by RNA interference or pharmacological inhibition with diclofenac reduced lactic acid levels in the culture supernatant. Furthermore, MCT4 expression in canine iUC tissues was positively correlated with infiltrating regulatory T cell (Treg) counts. Functional studies revealed that lactic acid promoted Treg differentiation and suppressed IFN-γ production by effector T cells. These findings indicate that MCT4 mediates lactic acid efflux from glycolytic tumor cells, contributing to the suppression of antitumor immunity. Targeting tumor metabolism through MCT4 inhibition may represent a promising therapeutic strategy for canine iUC. Therefore, insights from the metabolic and immunological landscape of canine iUC may inform the development of translational therapies for both veterinary and human oncology.

Sweet like chocolate: researching in the shade of a cacao tree

Reward network mechanism in anhedonia and depression

Background Depression is one of the most burdensome mental disorders. Anhedonia, a core symptom of major depressive disorder (MDD), is characterized by abnormal resting-state reward network (RN). However, it is unclear whether anhedonia symptom and depressive episode share similar resting-state RN mechanism, as well as whether the RN mechanism is a state or trait-like marker of depression. This study aims to clarify the two points by recruitingboth current and remitted depression. Methods Using functional Magnetic Resonance Imaging (fMRI) scans, this study observed the resting-state RN function connectivity (with the seed of ventral striatum) in patients with remitted depression (RMD, n = 27) and current depression (n = 30) and 33 normal controls. The low-frequency fluctuation (ALFF) and T1 image were further analyzed. Results Three groups differed in anhedonia scores, with highest anhedonia in the MDD group and lowest anhedonia in the NC group. In total sample, higher anhedonia was correlated with weaker connectivity between the striatum seed and the putamen, inferior frontal cortex, insula, AC, and thalamus, while in the RMD group, anhedonia correlated with higher AC, thalamus, and caudate connectivity. In resting-state function connectivity, the MDD group possessed weaker connectivity between the striatum seed and inferior frontal cortex and insula, while the RMD group showed weaker connectivity with the caudate, and both the MDD and RMD groups possessed lower connectivity with the AC. ALFF data indicated a higher anterior cingulate (AC) activation in the MDD group than the RMD group.T1 image indicated a bigger thalamus volume in the MDD group than the RMD group. Conclusions The current study is among the first to confirm that RMD patients possess different RN pattern compared with MDD. Importantly, caudate playsa unique role in depression remission, AC and thalamus mechanisms are trait-like markers of depression. Surprisingly, insula and inferior frontal mechanisms share by depressive episode and anhedonia, while putamen discriminates depressive episode and anhedonia. The results suggest candidate biomarkers for the treatment of clinical depression.

GPS timekeeping is increasingly vulnerable: here’s how to deliver future-proofed time

Robust emotion recognition for complex environments: ChildEmoNet model based on DETR-ResNet50 cascaded architecture

Emotion recognition faces significant challenges in complex real-world environments, particularly under facial occlusion conditions that severely impact traditional deep learning approaches. This research proposes ChildEmoNet, a novel cascaded emotion recognition framework that strategically integrates Detection Transformer (DETR) for robust multi-person detection with ResNet50 for discriminative feature extraction. The primary contributions include the development of a cascaded DETR-ResNet50 architecture that addresses both detection and classification challenges simultaneously, enhanced robustness mechanisms specifically designed for facial occlusion scenarios, and comprehensive evaluation across both categorical and dimensional emotion recognition tasks. Extensive experiments on the OMG Emotion Dataset demonstrate the effectiveness of this integration: the proposed model achieves an AUC of 0.93 in standard emotion classification tasks, maintains 79% recognition accuracy under 30% facial occlusion conditions, and attains concordance correlation coefficients (CCC) of 0.52 and 0.46 for valence and arousal prediction, respectively. The experimental validation confirms the crucial role of the DETR module in processing multi-person scenarios and the effectiveness of ResNet50 in feature extraction, demonstrating superior performance across complex environmental conditions including varying lighting, face orientations, and partial occlusions. Compared with traditional methods, this cascaded architecture shows remarkable robustness under challenging real-world conditions. This research advances emotion computing technology by providing a robust solution for emotion recognition applications in complex environments where conventional approaches exhibit significant performance degradation.

Sugarcane stem node detection with algorithm based on improved YOLO11 channel pruning with small target enhancement

Sugarcane stem node detection is critical for monitoring sugarcane growth, enabling precision cutting, reducing spuriousness, and improving breeding for resistance to downfall. However, in complex field environments, sugarcane stem nodes often suffer from reduced detection accuracy due to background interference and shadowing effects. For this reason, this paper proposes an improved sugarcane stem node detection model based on YOLO11. This study incorporates the ASF-YOLO (Attentional Scale Sequence Fusion based You Only Look Once) mechanism to enhance the feature fusion layer of YOLO11. Additionally, a high-resolution detection layer, P2, is integrated into the fusion module to improve the model’s ability to detect small objects—particularly sugarcane stem nodes—and to better handle multi-scale feature representations. Secondly, to better align with the P2 small-object detection layer, this paper adopts a shared convolutional detection head named LSDECD (Lightweight Shared Detail-Enhanced Convolutional Detection Head), which can better deal with small target detection while reducing the number of model parameters through parameter sharing and detail-enhanced convolution. Using soft-NMS (non-maximum suppression) to replace the original NMS and combining with Shape-IoU, a bounding box regression method that focuses on the shape and scale of the bounding box itself, makes the bounding box regression more accurate, and solves the problem of the impact of detection caused by occlusion and illumination. Finally, to address the increased complexity introduced by the addition of the P2 detection layer and the replacement of the detection head, channel pruning is applied to the model, effectively reducing its overall complexity and parameter count. The experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95, respectively, which are 11.9% and 11.1% higher than the original YOLO11n, and the model after pruning also has 10.8% and 9.3% higher than the original YOLO11n, respectively, and the number of parameters is reduced to 279,778, and model size is reduced to 1.3MB. The computational cost decreased from 11.6 GFlops to 6.6 GFlops.

The role of educational attainment and quality in U.S. regional variation in prevalence of dementia and CIND

There are striking disparities in dementia prevalence across regions of the U.S. Education is one of the most important risk factors for dementia. Level and quality of education varied geographically for older cohorts of U.S. adults, potentially contributing to regional differences in dementia prevalence. This study links historical state education quality data to respondents ages 65 and older in the Health and Retirement Study to determine the extent to which geographic disparities in dementia and cognitive impairment with no dementia (CIND) can be attributed to state-level measures of education quality. Older adults educated in states with better resourced education systems had lower prevalence of dementia (Relative risk ratio (RRR): 0.81; CI: 0.75, 0.87) and CIND (RRR: 0.89; CI: 0.84, 0.93), while those educated in states where more school funding came from state rather than local sources had higher prevalence of dementia (RRR: 1.12; CI: 1.01, 1.23) and CIND (RRR: 1.10; CI: 1.03, 1.16). Educational attainment does not explain the higher prevalence of dementia or CIND in the US South, but state-level education quality fully accounted for higher prevalence of dementia and CIND in the South. Finally, state-level education quality indicators were more strongly associated with dementia and CIND among those with less education These findings suggest education does in fact explain regional disparities in dementia and cognitive impairment, particularly between the South and other regions, but that it is the educational environment that matters more for geographic differences than educational attainment in these cohorts.

Mirror of the unknown: should research on mirror-image molecular biology be stopped?

Molecular epidemiology and phylogenetics of camel anaplasmosis

Camel anaplasmosis is a tick-borne disease of zoonotic concern, yet its epidemiology in Pakistan remains understudied. This study aimed to determine the prevalence, associated risk factors, and phylogenetic characteristics of Anaplasma spp. in camels across diverse agro-climatic zones of Punjab. A total of 400 blood samples were collected from two districts—Jhang and Bahawalpur (n = 200 each)—using a multistage cluster sampling approach. From each district, four tehsils were selected; ten herds per tehsil were sampled, with five camels per herd. The PCR targeting the 16S rRNA gene was used for Anaplasma detection. Epidemiological data were gathered using a structured questionnaire. The overall prevalence was 25.75%. Multivariable analysis identified age (>5 years), district (Jhang), intensive management, and health status as significant risk factors. Phylogenetic analysis revealed that A. phagocytophilum isolates were genetically related to strains from India, Iran, and Turkey; A. platys showed proximity to dog-derived isolates from India, South Africa, and Spain; while Candidatus A. camelii was closely related to camel isolates from Egypt, China, Kenya, and Iran. In conclusion, camel anaplasmosis is prevalent in Punjab. Further research is warranted to explore the pathogenic potential and vector dynamics of circulating strains to devise control strategies.

‘Lipstick on a pig’: how to fight back against a peer-review bully

Author Correction: Disease-associated astrocyte epigenetic memory promotes CNS pathology

Efficiency analysis of 67 Chinese research universities considering inter-university heterogeneity: Evidence from a meta-frontier network SBM DEA model

With the continuous increase in technology research and development investment, the overall operational efficiency of universities as the main body of scientific research has always been a focus of research. This study evaluates the efficiency of technology transfer in 67 Chinese universities directly affiliated with the Ministry of Education from 2016 to 2020. By integrating the meta-frontier analysis with the network Slacks-based Measure (SBM) Data Envelopment Analysis (DEA) approach, we assess the overall efficiency, stage-specific efficiency, and sources of inefficiency across different types of universities. Results indicate that while some institutions operate at the optimal frontier, the overall efficiency remains moderate, with the Technology Transfer and Application (TTA) stage consistently underperforming compared to the R&D stage. Significant heterogeneity exists among university types: normal, and medical & pharmaceutical universities demonstrate higher efficiency levels, whereas comprehensive, science and engineering, and agricultural and forestry universities exhibit notable inefficiencies, particularly in the TTA stage. Further decomposition reveals that technological gaps are the dominant source of inefficiency, especially in the later stage of the innovation process. Based on these findings, we propose targeted policy recommendations aimed at improving infrastructure, enhancing management practices, and tailoring reform strategies according to institutional type. This study contributes to the understanding of internal inefficiencies in university-led technology transfer and provides practical insights for policymakers and university administrators seeking to enhance the commercialization of academic research.

An improved FOX optimization algorithm using adaptive exploration and exploitation for global optimization

Optimization algorithms are essential for solving many real-world problems. However, challenges such as getting trapped in local minima and effectively balancing exploration and exploitation often limit their performance. This paper introduces an improved variation of the FOX optimization algorithm (FOX), termed Improved FOX (IFOX), incorporating a new adaptive method using a dynamically scaled step-size parameter to balance exploration and exploitation based on the current solution’s fitness value. The proposed IFOX also reduces the number of hyperparameters by removing four parameters (C1, C2, a, Mint) and refines the primary equations of FOX. To evaluate its performance, IFOX was tested on 20 classical benchmark functions, 61 benchmark test functions from the congress on evolutionary computation (CEC), and ten real-world problems. The experimental results showed that IFOX achieved a 40% improvement in overall performance metrics over the original FOX. Additionally, it achieved 880 wins, 228 ties, and 348 losses against 16 optimization algorithms across all involved functions and problems. Furthermore, non-parametric statistical tests, including the Friedman and Wilcoxon signed-rank tests, confirmed its competitiveness against recent and state-of-the-art optimization algorithms, such as LSHADE and NRO, with an average rank of 5.92 among 17 algorithms. These findings highlight the significant potential of IFOX for solving diverse optimization problems, establishing it as a competitive and effective optimization algorithm.

Trends and projections of polycystic ovary syndrome burden in China: Insights from the Global Burden of Disease Study 2021

Background Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder among women of reproductive age, associated with reproductive, metabolic, and psychological complications. In China, the burden of PCOS remains poorly characterized, particularly amid changing demographics and lifestyle patterns. This study evaluates trends in PCOS incidence, prevalence, and disability-adjusted life-years (DALYs) from 1990 to 2021 and projects the future burden through 2035. Methods Data on PCOS incidence, prevalence, and DALYs for Chinese women aged 10–54 years were extracted from the Global Burden of Disease Study 2021. Age-specific and age-standardized rates were calculated. Temporal trends were assessed using estimated annual percentage changes (EAPCs), and decomposition analysis quantified contributions of epidemiological changes, population growth, and aging. Projections through 2035 were based on current trends. Results From 1990 to 2021, PCOS incidence and prevalence showed significant increases, especially in younger age groups. Among 10–14-year-olds, incidence rose from 73,615 cases (95% UI: 35,399−124,529) to 128,219 cases (95% UI: 65,776–211,113), while prevalence increased from 124,220 (95% UI: 59,649–211,274) to 216,398 cases (95% UI: 110,028–357,026). Age-standardized rates are projected to rise to 70.82 (95% CI: 45.39–96.26) and 1,661.80 (95% CI: 1,467.99–1,855.62) per 100,000 by 2035, respectively. Decomposition analysis showed epidemiological changes as the primary driver of increased burden. Conclusions The burden of PCOS in China has risen substantially over three decades and is projected to escalate further. Marked increases in PCOS incidence and prevalence were observed among younger age groups, indicating an earlier onset or diagnosis. These findings highlight a shifting burden toward younger age groups and underscore the importance of age-specific surveillance and prevention strategies to address the evolving epidemiology of PCOS in China.

How to help refugees thrive: have local families host them

Identifying determinants of readmission and death post-stroke using explainable machine learning

Background Stroke remains a global health challenge with high rates of mortality and rehospitalization placing significant demands on healthcare systems. Identifying factors that determine outcomes of post-hospitalization improves resource allocation. Traditional statistical prediction models are suboptimal for the analysis of complex, multi-dimensional datasets. The objective of our study is to define the extended list of clinical and non-clinical predictors, which we believe can be achieved using Explainable Machine Learning (XML) models as an expansion of conventional methods. Methods We evaluated 11 established XML models that represent key ML methodologies to predict 90-day outcomes, namely mortality and rehospitalization among stroke survivors. The study population are 1,300 post-stroke individuals enrolled in the Transitions of Care Stroke Disparities Study (TCSD-S) (NIH/NIMH, NCT03452813) between June 2018 – October 2022. The care after transition data is sourced from participating comprehensive stroke centers and from the Florida Stroke Registry. The analysis incorporated clinical (e.g., age, stroke severity, comorbidities) and non-clinical factors including Social Drivers of Health (SDOH). A combined ranking approach, using Weighted Importance Scores and Frequency Counts, identified significant predictors across models. Results The resulting list of selected predictors included both established clinical factors and non-clinical factors, which enhanced prediction accuracy. Out of 38 identified predictors, 20 are non-clinical variables reflecting the importance of SDOH, environmental factors, and behavioral modifications beyond traditional clinical predictors of death/readmission. A secondary analysis restricted to ischemic stroke patients (n = 1,038) yielded virtually identical predictive performance, indicating robustness of the model within this subgroup. Conclusions Integrating SDOH, environmental factors, and behavioral modifications alongside traditional clinical predictors enhances the predictive accuracy of post-stroke outcome models. This underscores the critical role of addressing socioeconomic disparities during post-stroke transitions of care. Moreover, XML models’ ability to identify predictors spanning clinical and non-clinical domains suggests their potential to guide recovery. The resulting predictors are crucial for post-hospital care and hold strong potential for identifying individuals at risk of stroke, making them potentially significant across pre-stroke and hospitalization stages.

E-cigarette or vaping product use–associated lung injury outbreak and public perceptions and trends in smoking cessation discussions on Twitter

Background Cigarette smokers often use e-cigarettes to quit smoking. The outbreak of e-cigarette, or vaping, product use-associated lung injury (EVALI) in 2019 summer sparked discussions about vaping. However, there is a gap in exploring EVALI’s impact on discussions related to smoking cessation and vaping in smoking cessation on social media. Objective This study examines trends, sentiments, and topics in smoking cessation discussions before, during, and after EVALI. Methods English tweets from September 1, 2018, to January 31, 2020, were collected using snscrape, filtered for smoking-cessation-related keywords. Sentiments were assessed with Valence Aware Dictionary and sEentiment Reasoner (VADER), categorizing tweets as positive, negative, or neutral. Topics were identified via Latent Semantic Analysis, and LexRank was used to extract representative sentences for qualitative insights into the discussions. Results Among 397,528 smoking cessation-related tweets, discussions significantly increased in September 2019, accompanied by a decline in sentiment scores. Five topic groups—“Vaping”, “Cannabis”, “Stop Smoking”, “Gum”, and “Tobacco”—were identified. “Vaping” dominated the entire timespan, surpassing other topics in volume. Sentiment scores decreased for “Vaping”, “Stop Smoking”, and “Cannabis” in September 2019, while remaining stable for “Gum” and “Tobacco”. Representative sentences showed that despite the EVALI outbreak, many individuals still perceived vaping as an effective smoking cessation tool and oppose tobacco control policies targeting e-cigarette flavors. Conclusions The results deepen our comprehension of how perceptions regarding smoking cessation evolve during EVALI, offering insights for refining public health communication strategies in future health crises.

Author Correction: Complex genetic variation in nearly complete human genomes

INTerest of electrophysiological and functional EXploration in the evaluation of symptomatic impact of superior semicircular canal DEHIscence syndrome (INTEX-DEHI study): Study protocol for a reliability and validity study

Background Interest in Superior Semicircular Canal Dehiscence (SSCD) has increased, but its diagnosis and management remain challenging. Despite advances in imaging and electrophysiological tests, many patients face long diagnostic delays and significant distress due to unclear symptom severity. While some patients show no symptoms despite radiological evidence, others experience disabling effects. This research aims to bridge the gap between objective tests and symptom severity, improving the understanding and management of SSCD. Methods This is a prospective, non-randomized, longitudinal observational clinical study including repeated measures. It is part of the inter-regional hospital research program “INTEX-DEHI”, involving five university hospitals. Patients diagnosed with unilateral superior semicircular canal dehiscence via CT scan will be followed over four weeks with three visits spaced 15 days apart. The main objective is to describe the clinical and paraclinical characteristics of SSCD and analyze their correlation. Electrophysiological, functional, and clinical measurements will be collected at each visit. Discussion The relationship between electrophysiological tests and the symptomatic impact of superior semicircular canal dehiscence remains underexplored. This research aims to bridge this gap by studying both common and specific VEMP frequencies, as well as EcoG and WBT, to assess their sensitivity, specificity, and diagnostic value. While this study proposes innovative tools to objectively assess symptoms, challenges remain, particularly the underdiagnosis of the syndrome and selection biases, especially in patients with fluctuating symptoms. Additionally, the diversity of clinical presentations and the absence of symptoms in some patients complicate the interpretation of results. Trial registration Clinicaltrials.gov, NCT06170398.