Background/Aims: Previous studies have identified a substantial degree of agreement between the non-alcoholic fatty liver disease (NAFLD) and metabolic dysfunction-associated steatotic liver disease (MASLD) populations, but the same notion may not apply to normal-weight patients with a lower cardiometabolic risk burden. This study aims to investigate the cardiometabolic risk factor (CMRF) distributions between normal-weight and overweight/obese MASLD, the agreement between historical NAFLD and MASLD, and to compare the risk of liver-related events (LREs) and all-cause mortality in normal-weight versus overweight or obese MASLD. Methods: This study included participants with steatotic liver disease (SLD) from five cohorts in China (Hong Kong), South Korea, and the United States. Participants were recruited from settings including both hospitals and communities. Individuals were classified into normal-weight and overweight/obese groups. Results: This study included 33,793 participants with SLD from five cohorts, of whom 20,893 and 20,701 patients met the diagnosis of NAFLD and MASLD, respectively. Normal-weight patients with NAFLD demonstrated a lower CMRF distribution compared to those with overweight/obese NAFLD. In the community-based cohorts, the proportions with 0 CMRF ranged from 9.0 to 26.7% among normal-weight NAFLD patients, representing the discrepancy between MASLD and NAFLD definitions. Compared with the overweight/obese MASLD, the normalweight MASLD had increased all-cause mortality (normal-weight vs. overweight/obese, 23.44 and 13.80 per 1,000 person-years; P<0.001) but not LREs (2.81 and 2.59 per 1,000 person-years; P=0.54) in the Hong Kong Clinical Data Analysis and Reporting System cohort. Conclusions Normal-weight individuals with NAFLD demonstrated a lower distribution of CMRFs, resulting in the incomplete agreement between historical NAFLD and MASLD.

ObjectiveTo verify the technical feasibility and safety of partial nephrectomy in experimental pigs using a single-port endoscopic surgical robot in a transcontinental ultra-remote environment spanning 13 000 km with an average network delay of 204 ms, and to explore a dynamic combined anesthesia strategy that can effectively prevent intraoperative body movement in animals. MethodsThe research team used one experimental pig in Shanghai, China, and one in Orlando, the United States, and performed bidirectional remote surgery using the same model of single-port endoscopic surgical robot system. The anesthesia regimen included preoperative administration of 0.02 mg/kg dexmedetomidine hydrochloride and 0.05 mg/kg fentanyl citrate for combined sedation and analgesia. During remote surgery, according to the animal's reflex activity, pain response, and train-of-four stimulation (TOF) count results, vecuronium bromide was continuously infused at an initial rate of 0.04 mg·kg^-1·h^-1, and the dose was dynamically adjusted. Together with isoflurane inhalation anesthesia, the animal was maintained in a deep neuromuscular block state (TOF=0) to prevent intraoperative body movement. In addition, heart rate, blood pressure, muscle relaxation, and other indicators were monitored throughout the operation. ResultsSurgical operations were successfully performed at both sites, and the network delay remained stable at 202-208 ms. There was no data packet loss and no robot equipment failure. The vital signs of the experimental animal in Shanghai, China, remained stable, with zero body movement throughout the operation. Intraoperative blood loss was controllable, and no abnormal bleeding occurred. ConclusionIn transcontinental ultra-remote robotic surgery with inherent network delay, a dynamically adjusted anesthesia strategy can effectively avoid accidental bleeding caused by animal body movement and reduce the risks associated with network latency, thereby verifying the feasibility of this technical approach. In the future, it will be necessary to build a real-time cross-regional physiological data synchronization platform, standardize anesthesia protocols for animals undergoing remote surgery, and provide a technical basis for clinical translation to human trials.

Hepatic stellate cells (HSCs) are the primary fibrogenic cells in the liver, and their activation plays a crucial role in the development and progression of hepatic fibrosis. Here, we report that retinoid X receptor-alpha (RXRα), a unique member of the nuclear receptor superfamily, is a key modulator of HSC activation and liver fibrosis. RXRα exerts its effects by modulating calcium/calmodulin-dependent protein kinase kinase β (CaMKKβ)-mediated activation of AMP-activated protein kinase-alpha (AMPKα). In addition, we demonstrate that K-80003, which binds RXRα by a unique mechanism, effectively suppresses HSC activation, proliferation, and migration, thereby inhibiting liver fibrosis in the CCl₄ and amylin liver NASH (AMLN) diet animal models. The effect is mediated by AMPKα activation, promoting mitophagy in HSCs. Mechanistically, K-80003 activates AMPKα by inducing RXRα to form condensates with CaMKKβ and AMPKα via a two-phase process. The formation of RXRα condensates is driven by its N-terminal intrinsic disorder region and requires phosphorylation by CaMKKβ. Our results reveal a crucial role of RXRα in liver fibrosis regulation through modulating mitochondrial activities in HSCs. Furthermore, they suggest that K-80003 and related RXRα modulators hold promise as therapeutic agents for fibrosis-related diseases.

Drug resistance is one of the key factors affecting the effectiveness of cancer treatment methods, including chemotherapy, radiotherapy, and immunotherapy. Its occurrence is related to factors such as mRNA expression and methylation within cancer cells. If drug resistance in patients can be accurately identified early, doctors can devise more effective treatment plans, which is of great significance for improving patients' survival rates and quality of life. Cancer drug resistance prediction based on artificial intelligence (AI) technology has emerged as a current research hotspot, demonstrating promising application prospects in guiding clinical individualized and precise medication for cancer patients. This review aims to comprehensively summarize the research progress in utilizing AI algorithms to analyze multi-omics data including genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and histopathology, for predicting cancer drug resistance. It provides a detailed exposition of the processes involved in data processing and model construction, examines the current challenges faced in this field and future development directions, with the aim of better advancing the progress of precision medicine.

Acute lung injury (ALI) has been a kind of acute and severe disease that is mainly characterized by systemic uncontrolled inflammatory response to the production of huge amounts of reactive oxygen species (ROS) in the lung tissue. Given the critical role of ROS in ALI, a Fe₃O₄ loaded bovine serum albumin (BSA) nanocluster (BF) was developed to act as a nanomedicine for the treatment of ALI. Combining with NIR irradiation, it exhibited excellent ROS scavenging capacity. Significantly, it also displayed the excellent antioxidant and anti-inflammatory functions for lipopolysaccharides (LPS) induced macrophages (RAW264.7), and Sprague Dawley rats via lowering intracellular ROS levels, reducing inflammatory factors expression levels, inducing macrophage M2 polarization, inhibiting NF-κB signaling pathway, increasing CD4⁺/CD8⁺ T cell ratios, as well as upregulating HSP70 and CD31 expression levels to reprogram redox homeostasis, reduce systemic inflammation, activate immunoregulation, and accelerate lung tissue repair, finally achieving the synergistic enhancement of ALI immunotherapy. It finally provides an effective therapeutic strategy of BF + NIR for the management of inflammation related diseases.

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The International System for Human Cytogenomic Nomenclature (ISCN) is a standardized international nomenclature system established by the International Standing Committee on Human Cytogenomic Nomenclature (ISCN SC). It is designed for describing chromosomal or genomic abnormalities detected by commonly used genetic and genomic techniques including but not limited to karyotyping, fluorescence in situ hybridization, microarray, genome mapping, various region-specific assays, and high-throughput sequencing. With a history spanning over six decades, the ISCN was revised by the ISCN SC in 2024 and officially published in September 2024. This article provides a summary for the updates introduced in the 2024 edition of the International System for Human Cytogenomic Nomenclature.
Humans ; Terminology as Topic ; Genomics ; Genome, Human/genetics*

Medication reconciliation plays a key role in improving patient medication safety,reducing inappropriate polypharmacy,and promoting the high-quality development of pharmaceutical services.Compared to advanced international guidelines,China's medication reconciliation service standards have deficiencies in areas such as definition and process design,and multidisciplinary team building.There is a need to establish a comprehensive medication reconciliation effect evaluation index system,develop pharmacist-led multidisciplinary teams,promote the advancement of artificial intelligence and big data technologies,and strengthen outpatient and community medication reconciliation coverage,thereby contributing to the high-quality development of pharmaceutical services in China.

The International System for Human Cytogenomic Nomenclature (ISCN) is a standardized international nomenclature system established by the International Standing Committee on Human Cytogenomic Nomenclature (ISCN SC). It is designed for describing chromosomal or genomic abnormalities detected by commonly used genetic and genomic techniques including but not limited to karyotyping, fluorescence in situ hybridization, microarray, genome mapping, various region-specific assays, and high-throughput sequencing. With a history spanning over six decades, the ISCN was revised by the ISCN SC in 2024 and officially published in September 2024. This article provides a summary for the updates introduced in the 2024 edition of the International System for Human Cytogenomic Nomenclature.

In managing metabolic dysfunction-associated steatotic liver disease, which affects over 30% of the general population, effective noninvasive biomarkers for assessing disease severity, monitoring disease progression, predicting the development of liver-related complications, and assessing treatment response are crucial. The advantage of simple fibrosis scores lies in their widespread accessibility through routinely performed blood tests and extensive validation in different clinical settings. They have shown reasonable accuracy in diagnosing advanced fibrosis and good performance in excluding the majority of patients with a low risk of liver-related complications. Among patients with elevated serum fibrosis scores, a more specific fibrosis and imaging biomarker has proved useful to accurately identify patients at risk of liver-related complications. Among specific fibrosis blood biomarkers, enhanced liver fibrosis is the most widely utilized and has been approved in the United States as a prognostic biomarker. For imaging biomarkers, the availability of vibration-controlled transient elastography has been largely improved over the past years, enabling the use of liver stiffness measurement (LSM) for accurate assessment of significant and advanced fibrosis, and cirrhosis. Combining LSM with other routinely available blood tests enhances the ability to diagnose at-risk metabolic dysfunction-associated steatohepatitis and predict liver-related complications, some reaching an accuracy comparable to that of liver biopsy. Magnetic resonance imaging-based modalities provide the most accurate quantification of liver fibrosis, though the current utilization is limited to research settings. Expanding their future use in clinical practice depends on factors such as cost and facility availability.