Transmembrane proteins (TMEM) are a type of membrane protein. Most proteins in this family are located in the phospholipid bilayer of the cell membrane, while a smaller portion is found in the membranes of cellular organelles. Transmembrane protein 43 (TMEM43) is a member of the TMEM protein family and is encoded by the TMEM43 gene. This protein consists of 400 amino acids and has 4 transmembrane domains and 1 membrane-associated domain. TMEM43 is localized to various biological membranes within the cell, such as the cell membrane and nuclear membrane, where it forms transmembrane channels for various ions. Additionally, TMEM43 is expressed in many species, showing high genetic similarity, especially with the four transmembrane domains being highly conserved. Current studies on the TMEM43 gene are still in its early stages, mainly focusing on its association with arrhythmogenic right ventricular cardiomyopathy (ARVC) and cancer. However, recent studies suggest that pathogenic mutations in TMEM43 may cause auditory neuropathy spectrum disorder (ANSD). Patients with TMEM43 p.Ser372Ter exhibited late-onset progressive ANSD. Impact of TMEM43 pathogenic mutations on individual hearing was likely mediated through effects on gap junction (GJ) structures on glia-like supporting cells (GLS), cell membranes. The TMEM43 p.Arg372Ter pathogenic mutation primarily affected the structure and function of TMEM43 protein, leading to premature termination of protein translation and the production of a truncated protein. Abnormal TMEM43 protein significantly reduced K⁺ influx in GLS cells, disrupting the endolymphatic K⁺ circulation and cochlear microenvironment homeostasis. When K⁺ circulation was obstructed, the endocochlear potential (EP) became abnormal, impairing the physiological function of hair cells and potentially leading to hearing impairment. However, it is important to note that studies on the mechanism is limited, and more experimental evidence is needed to confirm this hypothesis. Currently, there is a significant gap in research on TMEM43 and hearing loss, with many issues remaining unresolved. While TMEM43 has been studied in relation to hearing loss in humans, zebrafish, mice, and rats, the research is still preliminary. Detailed investigations into the molecular pathogenic mechanisms, the impact of mutations on hearing damage, and related therapeutic strategies are needed. Additionally, as a newly identified hearing loss-related gene, the mutation frequency and incidence of hearing disorders associated with TMEM43 have not been effectively quantified. For example, the ClinVar database listed 829 mutation sites for the TMEM43 gene, with only three mutations related to auditory neuropathy: c.605A>T (p.Asn202Ile), c.889T>A (p.Phe297Ile), and c.1114C>T (p.Arg372Ter). Aside from the aforementioned TMEM43 c.1114C>T (p.Arg372Ter) mutation observed in patients, the other two mutations were experimentally induced and have not been found in patients. Consequently, these mutations have been classified as unknown significance. We reviewed the current understanding of TMEM43 and hearing loss, analyzed its role in ear development and sound conduction, and explored the impact of TMEM43 gene variations on hearing loss, aiming to provide new insights for future research and precision medicine related to TMEM43.

Inflammatory diseases (IDs) are a general term of diseases characterized by chronic inflammation as the primary pathogenetic mechanism, which seriously affect the quality of patient′s life and cause significant social and medical burden. Current drugs for IDs include nonsteroidal anti-inflammatory drugs, corticosteroids, immunomodulators, biologics, and antioxidants, but these drugs may cause gastrointestinal side effects, induce or worsen infections, and cause non-response or intolerance. Given the outstanding performance of metal polyphenol network (MPN) in the fields of drug delivery, biomedical imaging, and catalytic therapy, its application in the diagnosis and treatment of IDs has attracted much attention and significant progress has been made. In this paper, we first provide an overview of the types of IDs and their generating mechanisms, then sort out and summarize the different forms of MPN in recent years, and finally discuss in detail the characteristics of MPN and their latest research progress in the diagnosis and treatment of IDs. This research may provide useful references for scientific research and clinical practice in the related fields.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

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.

Objective To address the current blind spots in quality control and the risk of data distortion in radiological health technical service institutions, overcome the limitations of low efficiency and insufficient anomaly identification in manual verification, establish an automatic anomaly early warning mechanism based on AI algorithms, provide accurate clues for health supervision and law enforcement, and significantly enhance the efficiency of industry supervision. Methods On-site inspection data were collected via photographic imaging. Advanced image processing and object detection algorithms were employed to automatically extract and analyze key information from the images. Through data analysis, the system was used to calculate and assess the inspection results. A dynamic early warning engine was developed to automatically trigger alerts upon detection of deviations from standard thresholds or regulatory violations in radiological health technical services. These alerts were delivered through a regulatory information system. Results Following AI model training, the accuracy of the image processing and object detection algorithms reached 99% and the early warning accuracy reached 93%. Compared with the traditional supervision mode, the efficiency of anomaly detection was improved and the average response time was shortened. Conclusion The full-process supervision information system constructed in this study has realized the triple mechanism of pre-event standardization, in-event monitoring, and post-event traceability for radiological health technical services. In particular, the system has established a four-level quality control chain of “institutional self-inspection, intelligent recheck, expert judgment, and administrative action”, providing a technological innovation path for ensuring the radiation protection and safety of medical staff, patients, and the public.

Objective: To explore the influencing factors for implementation of weight management services in primary medical and healthcare institutions, so as to provide references for implementing sustainable services of weight management. Methods: From May to June 2025, Pinghu City, Zhejiang Province was selected as the survey site. Personnel responsible for weight management in primary medical and healthcare institutions were selected as the survey subjects using a combined method of purposive sampling and snowball sampling. Based on the five core domains of the consolidated framework for implementation research (CFIR), a semi-structured interview outline for weight management services in primary medical and healthcare institutions was designed. Original data was collected through face-to-face semi-structured interviews. Interview data was organized and analyzed using framework analysis. Factors affecting weight management services were quantitatively analyzed by referencing CFIR's structural rating criteria. Results: A total of 21 participants completed interviews, covering positions in nutrition, endocrinology, traditional Chinese medicine, general practice, maternal health, and public health. There were 9 males and 12 females. Fifteen participants (71.43%) were aged 35 years and above, 18 (85.71%) held a bachelor's degree or higher, and 15 (71.43%) were frontline medical staff. Fifteen factors affecting weight management services were identified across five domains: innovation, outer setting, inner setting, individuals, and implementation process. Six barrier factors were identified: difficulties in policy implementation, time-consuming interventions, limited incentive measures, lack of professional skills, unclear weight-loss plans and goal setting, and imperfect follow-up and evaluation mechanisms. Three neutral factors were identified: the development and refinement of policies and regulations, the implementation of weight management training, and the optimization of the referral process within integrated healthcare systems (medical alliances / communities). Six facilitating factors were identified: the relatively significant advantages of lifestyle interventions, collaboration and coordination across multiple departments, cooperative communication among different units within the institution, the inherent convenience of primary care settings, a strong sense of professional responsibility, and the establishment of multidisciplinary teams. Conclusions The delivery of weight management services in primary medical and healthcare institutions is influenced by a wide array of factors across multiple domains. It requires policy support, multi-department coordination, a practice-oriented training system, optimized team resource allocation, incentives, and improved professional skills of medical staff to jointly promote long-term implementation.