Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

ObjectiveIn nature, objects cast shadows due to illumination, forming the basis for stereoscopic perception. Birds need to adapt to changes in lighting (meaning they can recognize stereoscopic shapes even when shadows look different) to accurately perceive different three-dimensional forms. However, how neurons in the key visual brain area in birds handle these lighting changes remains largely unreported. In this study, pigeons (Columba livia) were used as subjects to investigate how neurons in pigeon’s ventrolateral mesopallium (MVL) represent stereoscopic shapes consistently, regardless of changes in lighting. MethodsVisual cognitive training combined with neuronal recording was employed. Pigeons were first trained to discriminate different stereoscopic shapes (concave/convex). We then tested whether and how light luminance angle and surface appearance of the stereoscopic shapes affect their recognition accuracy, and further verify whether the results rely on specify luminance color. Simultaneously, neuronal firing activity of neurons was recorded with multiple electrode array implanted from the MVL during the presentation of difference shapes. The response was finally analyzed how selectively they responded to different stereoscopic shapes and whether their selectivity was affected by the changes of luminance condition (like lighting angle) or surface look. Support vector machine (SVM) models were trained on neuronal population responses recorded under one condition (light luminance angle of 45°) and used to decode responses under other conditions (light luminance angle of 135°, 225°, 315°) to verify the invariance of responses to different luminance conditions. ResultsBehavioral results from 6 pigeons consistently showed that the pigeons could reliably identify the core 3D shape (over 80% accuracy), and this ability wasn’t affected by changes in light angle or surface appearance. Statistical analysis of 88 recorded neurons from 6 pigeons revealed that 83% (73/88) showed strong selectivity for specific 3D shapes (selectivity index>0.3), and responses to convex shapes were consistently stronger than to concave shapes. These shape-selective responses remained stable across changes in light angle and surface appearance. Neural patterns were consistent under both blue and orange lighting. The decoding accuracy achieves above 70%, suggesting stable responses under different conditions (e.g., different lighting angles or surface appearance). ConclusionNeurons in the pigeon MVL maintain a consistent neural encoding pattern for different stereoscopic shapes, unaffected by illumination or surface appearance. This ensures stable object recognition by pigeons in changing visual environments. Our findings provide new physiological evidence for understanding how birds achieve stable perception (“invariant neural representations”) while coping with variations in the visual field.

Aim To establish a gait analysis system based on DeepLabCut(DLC)algorithm for evaluating motor function in aged mice.Methods Based on DLC algorithm in deep learning technology,treadmill device and fully closed design were used in the system,including software and hardware.This system was applied to evaluate gait characteristics of mice due to aging un-der different movement modes.Correlation analysis was used to explore the effects of body weight and body length on gait indica-tors.Results This system realized the synchronous analysis of three-dimensional gait(lateral and ventral plane)of mice at specific gait speed,and automatically quantified 47 gait indica-tors.Using this system,it was found that during walking(15 cm·s-1),the standard deviation of body turning angle decreased,forelimb sway duration,standard deviation of knee angle,mean outward angles of left and right hind paw increased in 8 and 15 month-old mice,compared with 2-month-old mice.However,15-month-old mice showed decreased walking frequency,and in-creased stride width,total duration of double support,and knee extension and contraction distance.In addition,at trot(20 cm·s-1),15-month-old mice were unable to walk steadily,and 8-month-old mice had increased total duration of double support and mean outward angles of left hind paw,compared with 2-month-old mice.Correlation analysis revealed that indicators like walking frequency,stride width,forelimb sway duration,total duration of double support,standard deviation of knee an-gle,knee extension and contraction distance,were not affected by changes in body weight and body length.Conclusions The gait analysis system based on DLC algorithm can achieve a more sensitive,accurate and comprehensive evaluation of the gait of aged mice,distinguishing the gait characteristics of aged mice to maintain gait stability,and selecting behavioral indicators that better reflect the gait changes of aged mice.It provides a meth-odological basis for more effective assessment of efficacy and side effects of drugs for anti-aging and anti-decline of motor coordina-tion in the future.

This study was aimed at exploring the epidemiological characteristics of plague,and the evolutionary relation-ships among the isolated plague strains in the Yunnan border area,to provide clues for further studying epidemic causes and ep-idemiological patterns.Plague epidemic data in the border area during the second epidemic period(1982-2007)were collected and analyzed with descriptive epidemiological methods.Whole genome sequences of 262 strains of Yersinia pestis in the border area were obtained for phylogenetic analysis.Plague outbreaks occurred in 17 counties(cities)among 25 border counties(cit-ies);a total of 552 epidemic foci and 123 human cases were identified.The 1.ORI2,1.ORI3,1.IN3,2.ANT and 2.MED geno-types were identified among Yersinia pestis isolated from the Yunnan border area,among which the 1.ORI2 population was dominant.A total of 258 strains of Yersinia pestis from the 1.OR12 population belonged to four subclusters.The Myanmar and Vietnam clade was embedded within the Yunnan clade in the overall phylogeny.The above results indicated that during the sec-ond period of the epidemic,the intensity of plague epidemics in Yunnan's border areas was high,showing a trend of devel-opment from west to south and east.Our findings indicated a risk of cross-border transmission of plague between Yunnan and neighboring countries;therefore,the surveillance,pre-vention,and control of plague in border areas should be strengthened.

This is the first reported case of a female with monkeypox infection in Kunming City,Yunnan Province.An epi-demiological investigation was conducted to provide a scientific basis for the prevention and control of monkeypox epidemics in China,especially for early detection in females in accordance with the"Monkeypox prevention and control program(2023 ver-sion)".Diagnosis was performed as described in the"Monkeypox Diagnosis and Treatment Guidelines(2022 version)".Speci-mens were collected for laboratory testing.The epidemiological investigation determined that the female patient had sexual in-tercourse with her newly married husband once before disease onset and the husband hid his history of male homosexual sex.The laboratory test results of the woman and her husband were positive for the nucleic acid of the monkeypox virus.Both had typical clinical symptoms,including rash.The epidemiological investigation,clinical symptoms,laboratory test results,and previous epidemic data of monkeypox in Yunnan province confirmed the woman as the first female infected with monkeypox in Yunnan Province and her husband was the presumed source of infection.

AIM To establish a UPLC-MS/MS method for the simultaneous content determination of hyperoside,kaempferol-3-O-rutinoside,esculin,chlorogenic acid,berberine,quercetin,rehmannioside D,palmatine,leonurin A,paeoniflorin,timosaponin BⅡand rutin in Guanhuangmu Granules.METHODS The analysis was performed on a 40℃ thermostatic Acquity UPLC HSS T3 column(100 mm×2.1 mm,1.8 μm),with the mobile phase comprising of 0.05%formic acid(containing 2 mmol/L ammonium acetate)-acetonitrile flowing at 0.3 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive and negative ion scanning.RESULTS Twelve constituents showed good linear relationships within their own ranges(r＞0.999 0),whose average recoveries were 93.46%-102.01%with the RSDs of 2.13%-3.65%.CONCLUSION This rapid,accurate,stable and sensitive method can be used for the quality control of Guanhuangmu Granules.