Adipose tissue is a critical energy reservoir in animals and humans, with multifaceted roles in endocrine regulation, immune response, and providing mechanical protection. Based on anatomical location and functional characteristics, adipose tissue can be categorized into distinct types, including white adipose tissue (WAT), brown adipose tissue (BAT), beige adipose tissue, and pink adipose tissue. Traditionally, adipose tissue research has centered on its morphological and functional properties as a whole. However, with the advent of single-cell transcriptomics, a new level of complexity in adipose tissue has been unveiled, showing that even under identical conditions, cells of the same type may exhibit significant variation in morphology, structure, function, and gene expression——phenomena collectively referred to as cellular heterogeneity. Single-cell transcriptomics, including techniques like single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), enables in-depth analysis of the diversity and heterogeneity of adipocytes at the single-cell level. This high-resolution approach has not only deepened our understanding of adipocyte functionality but also facilitated the discovery of previously unidentified cell types and gene expression patterns that may play key roles in adipose tissue function. This review delves into the latest advances in the application of single-cell transcriptomics in elucidating the heterogeneity and diversity within adipose tissue, highlighting how these findings have redefined the understanding of cell subpopulations within different adipose depots. Moreover, the review explores how single-cell transcriptomic technologies have enabled the study of cellular communication pathways and differentiation trajectories among adipose cell subgroups. By mapping these interactions and differentiation processes, researchers gain insights into how distinct cellular subpopulations coordinate within adipose tissues, which is crucial for maintaining tissue homeostasis and function. Understanding these mechanisms is essential, as dysregulation in adipose cell interactions and differentiation underlies a range of metabolic disorders, including obesity and diabetes mellitus type 2. Furthermore, single-cell transcriptomics holds promising implications for identifying therapeutic targets; by pinpointing specific cell types and gene pathways involved in adipose tissue dysfunction, these technologies pave the way for developing targeted interventions aimed at modulating specific adipose subpopulations. In summary, this review provides a comprehensive analysis of the role of single-cell transcriptomic technologies in uncovering the heterogeneity and functional diversity of adipose tissues.

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.

Norovirus is the global leading cause of epidemic and endemic acute gastroenteritis in people of all ages.To inves-tigate the genetic diversity of GⅡ genogroup noroviruses linked to clustered infections in northeast Chongqing,we collected anal swabs or environmental smears from 11 norovirus outbreaks during 2021-2022.Norovirus RNA was detected by quantitative real-time PCR(qRT-PCR),and partial viral RdRp/capsid genes were amplified by reverse transcription PCR(RT-PCR)and sequenced.Among samples from 11 outbreaks in 4 districts and counties,55 strains of GⅡ genogroup norovirus were detected.Six genotypes were identified with an online norovirus genotyping tool(http://www.rivm.nl/mpf/norovirus/typingtool).Genotype GⅡ.17[P17]was associated with four outbreaks;the co-circulating GⅡ.17[P17]and GⅡ.1[P16]caused another out-break;GⅡ.6[P7]and GⅡ.8[P8]respectively were linked to two outbreaks;and GⅡ.3[P12]and GⅡ.2[P16]respectively ac-counted for one outbreak.Phylogenetic analysis also indicated that 55 GⅡ genogroup strains formed five clusters,with norovir-uses of identical genotypes from diverse events belonging to the same cluster,and that genetically distinct genotypes from di-verse events belonged to different clusters.Therefore,our results revealed that multiple genotypes associated with norovirus outbreaks were circulating in northeast Chongqing,and GⅡ.17[P17]was the predominant genotype linked to these out-breaks during 2021-2022.Most norovirus outbreak events were caused by single sources,and genetic relationships were demonstrated among noroviruses of identical genotypes from diverse events.

Objective To investigate the prevalence of Helicobacter pylori(H.pylori)positivity in the urban areas of Chongqing and its relationship with age,season,and gender.Methods A total of 4,836 patients who underwent 14C-UBT testing at our hospital from April 2023 to March 2024 were selected.The detection rate of H.pylori and the infection status of H.pylori in different age groups,genders,and seasons were analyzed.Logistic regression analysis was performed on factors related to H.pylori infection identified in the univariate analysis.Results The positive detection rate of H.pylori in the 4,836 subjects was 29.3％,with a higher rate in males than in females,showing a statistically significant difference(P＜0.05).The highest positive detection rate of H.pylori was in the summer season,and the peak positivity rate for H.pylori was observed in the 30-39 age group.There was a statistically significant difference in the positive detection rate of H.pylori across seasons and among different age groups(P＜0.05).Multivariate logistic regression analysis indicated that gender,age,and season were all influential factors in the outcome of H.pylori infection(P＜0.05).Conclusion The prevalence of H.pylori infection in the urban areas of Chongqing is relatively low,and gender,age,and season are all influential factors for H.pylori infection.

Objective:To analyze the incidence and clinical phenotype of the concomitant extragenital malformations in the patients with female reproductive tract anomalies.Methods:A retrospective study was conducted using clinical data of hospitalized patients diagnosed with uterine, cervical, or vaginal malformations from January 2003 to December 2022 in Peking Union Medical College Hospital. The malformations were classified according to American Society for Reproductive Medicine müllerian anomalies classification 2021, and in each type, the incidence and specific manifestations of concomitant extragnital malformations were analyzed.Results:A total of 444 patients were included. The overall incidence of concomitant extragenital malformations was 43.5% (193/444), including urinary system, skeletal system, and other system malformations. Renal malformations on the obstructed side were present in all patients with oblique vaginal septum syndrome (100.0%, 78/78). The total incidence of concomitant extragnital malformations was as high as 8/11 in uterus didelphys, 43.5% (10/23) in unicornuate uterus, 33.6% (79/235) in Mayer-Rokitansky-Küster-Hauser syndrome, 18.8% (6/32) in septate uterus and 18.5% (12/65) in cervical agenesis. Urinary system malformations (30.6%, 136/444) and skeletal system malformations (13.5%, 60/444) were the most common concomitant malformations in all types, in which, unilateral renal agenesis and scoliosis were the most common.Conclusions:Urinary and skeletal system malformations are important features of female reproductive tract anomalies. Urologic ultrasonography and spinal roentgenogram are recommended for all patients with female reproductive tract anomalies.

Due to the limited reliability of traditional self-completed questionnaire, the accuracy of measurement of physical behaviors (physical activity, sedentary behavior and sleep) is not high. With the development of technology, wearable devices (e.g. accelerometer) can be used for more accurate measurement of physical behaviors and have great application potential in large-scale research. However, the data of objective measurement of physical behaviors from large-scale cohort research in Asian populations is still limited. Between August 2020 and December 2021, the 3 rd resurvey of China Kadoorie Biobank (CKB) project used Axivity AX3 wrist triaxial accelerometer to collect the data of participants' daily activity and sleep status. A total of 20 370 participants from 10 study areas were included in the study, in whom 65.2% were women, and the age was (65.4±9.1) years. The participants' physical activity level varied greatly in different study areas. The objective measurement of participants' physical behaviors in CKB project has provided valuable resources for the description of 24-hour patterns of physical behaviors and evaluation of the health effect of physical activity, sedentary behavior and sleep as well as their association with diseases in the elderly in China.