Genomic and Phenotypic Adaptations of Rattus tanezumi to Cold Limit Its Further Northward Expansion and Range Overlap with R. norvegicus.

Global climate change has led to shifts in the distribution ranges of many terrestrial species, promoting their migration from lower altitudes or latitudes to higher ones. Meanwhile, successful invaders have developed genetic adaptations enabling the colonization of new environments. Over the past 40 years, Rattus tanezumi (RT) has expanded into northern China (Northwest and North China) from its southern origins. We studied the cold adaptation of RT and its potential for northward expansion by comparing it with sympatric Rattus norvegicus (RN), which is well adapted to cold regions. Through population genomic analysis, we revealed that the invading RT rats have split into three distinct populations: the North, Northwest, and Tibetan populations. The first two populations exhibited high genetic diversity, while the latter population showed remarkably low genetic diversity. These rats have developed various genetic adaptations to cold, arid, hypoxic, and high-UV conditions. Cold acclimation tests revealed divergent thermoregulation between RT and RN. Specifically, RT exhibited higher brown adipose tissue activity and metabolic rates than did RN. Transcriptome analysis highlighted changes in genes regulating triglyceride catabolic processes in RT, including Apoa1 and Apoa4, which were upregulated, under selection and associated with local adaptation. In contrast, RN showed changes in carbohydrate metabolism genes. Despite the cold adaptation of RT, we observed genotypic and phenotypic constraints that may limit its ability to cope with severe low temperatures farther north. Consequently, it is less likely that RT rats will invade and overlap with RN rats in farther northern regions.

Inflammation-related pathology in the olfactory epithelium: its impact on the olfactory system in psychotic disorders.

Smell deficits and neurobiological changes in the olfactory bulb (OB) and olfactory epithelium (OE) have been observed in schizophrenia and related disorders. The OE is the most peripheral olfactory system located outside the cranium, and is connected with the brain via direct neuronal projections to the OB. Nevertheless, it is unknown whether and how a disturbance of the OE affects the OB in schizophrenia and related disorders. Addressing this gap would be the first step in studying the impact of OE pathology in the disease pathophysiology in the brain. In this cross-species study, we observed that chronic, local OE inflammation with a set of upregulated genes in an inducible olfactory inflammation (IOI) mouse model led to a volume reduction, layer structure changes, and alterations of neuron functionality in the OB. Furthermore, IOI model also displayed behavioral deficits relevant to negative symptoms (avolition) in parallel to smell deficits. In first episode psychosis (FEP) patients, we observed a significant alteration in immune/inflammation-related molecular signatures in olfactory neuronal cells (ONCs) enriched from biopsied OE and a significant reduction in the OB volume, compared with those of healthy controls (HC). The increased expression of immune/inflammation-related molecules in ONCs was significantly correlated to the OB volume reduction in FEP patients, but no correlation was found in HCs. Moreover, the increased expression of human orthologues of the IOI genes in ONCs was significantly correlated with the OB volume reduction in FEP, but not in HCs. Together, our study implies a potential mechanism of the OE-OB pathology in patients with psychotic disorders (schizophrenia and related disorders). We hope that this mechanism may have a cross-disease implication, including COVID-19-elicited mental conditions that include smell deficits.

First Report of the Rust Fungus Melampsora ferrinii on Salix babylonica in China and a New Spermogonial and Aecial Host, Corydalis bungeana.

The occurrence of rust fungi on Corydalis bungeana Turcz. and Salix babylonica L. were found in same area of Hebei Province, China from 2022 to 2023. The life cycle connection of these rust fungi was suspected because Peng et al. (2022) reported the life cycle of Melampsora ferrinii Toome & Aime by inoculations, producing spermogonia and aecia on Corydalis species, and uredinia on S. babylonica. The morphology of the uredinial and telial stages on S. babylonica collected in the field was identical with the description of M. ferrinii by Toome and Aime (2015), and its identity was confirmed by phylogenetic analyses using the method of Ji et al. (2020) (LSU-PP087777, ITS-PP091274; Similarity with M. ferrinii: LSU-100%, ITS-99.85%). To confirm the life cycle of this rust fungus, inoculations were conducted on C. bungeana with basidiospores obtained from the teliospores on fallen leaves of Salix babylonica. The fallen leaves producing basidiospores were cut into small pieces (ca. 5 mm2) and placed on healthy leaves of C. bungeana. The inoculated plants were kept in a moist plastic box in darkness at 15-20℃ for 2 days and then transferred to the floor near windows at about 15-20℃ for observations. Ten days after inoculations small yellow spots of spermogonia appeared on the upper surface of the leaves of C. bungeana. About 7 days later, pale yellow aecia with aeciospores were produced mainly on the under surface of the leaves and petioles. The morphology of rust fungus on C. bungeana collected from the fields and obtained by inoculations was identical with the description by Peng et al. (2022). Phylogenetic analyses also showed that a specimen on C. bungeana collected from the field (LSU-OR607838, ITS-OR612063) were included into the same clade of M. ferrinii (Similarity: LSU-100 %, ITS-99.85). Based on morphology, inoculations and DNA sequence analyses, the rust fungi on C. bungeana and S. babylonica are identified as different stages of life cycle of M. ferrinii. This rust fungus has been reported to produce spermogonia and aecia on C. acuminata Franch., C. edulis Maxim. and C. racemosa (Thunb.) Pers. in China (Peng et al. 2022), and uredinia and telia on S. babylonica in USA, Argentina and Iran (Toome and Aime 2015, Abbasi et al. 2024), and on Salix sp. in Chile (Zapata 2016). Therefore, C. bungeana is a new host for M. ferrinii, and its field occurrence on S. babylonica is reported for the first time in China although Peng et al. (2022) reported successful results in its inoculations to S. babylonica in China. This report contributes to the control of rust diseases caused by this species. Specimens used in this experiment were deposited in the Fungal Herbarium of the Jilin Agricultural University, Changchun, China (HMJAU) and sequences newly analyzed were deposited in GenBank.

[Development of an analytical system for dried blood spots for forensic toxicology: a case study of five common drugs and poisons].

Dried blood spot (DBS) technology is a simple and convenient method for collecting, transporting, and storing blood samples on filter paper, and has numerous applications in the clinical, research, and public health settings. This technique is gaining popularity in the field of forensic science because it facilitates the rapid analysis of prohibited drugs in blood samples and offers significant advantages in toxicology scenarios such as drinking-driving screening, drug abuse detection, and doping detection. However, the lack of a standardized system and the fact that its stability and reliability have not been thoroughly researched and demonstrated limit its application in judicial practice in China. DBS samples can be prepared, stored, and analyzed in various ways, all of which may significantly affect the results. In this study, we developed a method based on ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) that focuses on the preparation, pretreatment, analysis, and storage of DBS samples. A thorough investigation was conducted to examine the optimal preparation conditions, including the blood spot matrix, drying technique, and preprocessing parameters, such as the solvent and extraction method. Moreover, the analytical conditions, such as the mobile phase system and elution gradient, were established to facilitate the quantitative detection of methamphetamine, lidocaine, ketamine, fentanyl, and diazepam in both DBS and whole-blood samples. The impact of storage conditions, such as the temperature, humidity, and sealing, on the analytical results of the DBS and whole-blood samples was also examined. The results showed a strong linear relationship for lidocaine and fentanyl within the range of 0.5-100 ng/mL. Similarly, methamphetamine, ketamine, and diazepam exhibited good linearity within the range of 2-100 ng/mL. The coefficients of determination (r2) ranged from 0.9983 to 0.9997, and the limits of detection ranged from 0.2 to 0.5 ng/mL, indicating a high degree of correlation and sensitivity. Stability tests demonstrated that the five target substances remained stable in the DBS for 60 days, with the measured contents deviating from the nominal values by 15%. Moreover, the measurement results of the DBS samples were highly similar to those of the whole-blood samples, with mean percentage differences of 4.44%, 3.50%, 7.66%, 5.10%, and 5.25% for fentanyl, diazepam, ketamine, lidocaine, and methamphetamine, respectively. Throughout the 60-day storage period, the maintenance of temperatures of -20 and 4 ℃, as well as sealing and dry storage, was not necessary. Room temperature was the most practical storage environment for the DBS samples. The results for each target showed very small concentration differences between the whole-blood and DBS samples, indicating that the DBS samples were suitable for drug and poison analysis in blood. Furthermore, the DBSs exhibited high quantitative consistency with the whole-blood samples, rendering them suitable matrices for preserving blood samples. Because DBS samples are easy to handle and store, they can realize the lightweight preservation of blood samples and provide a novel solution for the analysis and preservation of blood samples in public security practice. We recommend conducting comprehensive validations before utilizing DBS for analysis, particularly in terms of quantification, to ensure the judicial reliability of the results.

Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.

PURPOSE: Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer. METHODS AND MATERIALS: Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors. RESULTS: The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit. CONCLUSION: Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

Neural substrates for regulating self-grooming behavior in rodents. / 啮齿动物自我梳理行为调控的神经基质.

Grooming, as an evolutionarily conserved repetitive behavior, is common in various animals, including humans, and serves essential functions including, but not limited to, hygiene maintenance, thermoregulation, de-arousal, stress reduction, and social behaviors. In rodents, grooming involves a patterned and sequenced structure, known as the syntactic chain with four phases that comprise repeated stereotyped movements happening in a cephalocaudal progression style, beginning from the nose to the face, to the head, and finally ending with body licking. The context-dependent occurrence of grooming behavior indicates its adaptive significance. This review briefly summarizes the neural substrates responsible for rodent grooming behavior and explores its relevance in rodent models of neuropsychiatric disorders and neurodegenerative diseases with aberrant grooming phenotypes. We further emphasize the utility of rodent grooming as a reliable measure of repetitive behavior in neuropsychiatric models, holding promise for translational psychiatry. Herein, we mainly focus on rodent self-grooming. Allogrooming (grooming being applied on one animal by its conspecifics via licking or carefully nibbling) and heterogrooming (a form of grooming behavior directing towards another animal, which occurs in other contexts, such as maternal, sexual, aggressive, or social behaviors) are not covered due to space constraints.