Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Nusinersen effectiveness and safety in pediatric patients with 5q-spinal muscular atrophy: a multi-center disease registry in China.

OBJECTIVE: To evaluate the effectiveness and safety of nusinersen for the treatment of 5q-spinal muscular atrophy (SMA) among Chinese pediatric patients. METHODS: Using a longitudinal, multi-center registry, both prospective and retrospective data were collected from pediatric patients with 5q-SMA receiving nusinersen treatment across 18 centers in China. All patients fulfilling the eligibility criteria were included consecutively. Motor function outcomes were assessed post-treatment by SMA type. Safety profile was evaluated among patients starting nusinersen treatment post-enrollment. Descriptive analyses were used to report baseline characteristics, effectiveness, and safety results. RESULTS: As of March 2nd, 2023, 385 patients were included. Most patients demonstrated improvements or stability in motor function across all SMA types. Type II patients demonstrated mean changes [95% confidence interval (CI)] of 4.4 (3.4-5.4) and 4.1 (2.8-5.4) in Hammersmith Functional Motor Scale-Expanded (HFMSE), and 2.4 (1.7-3.1) and 2.3 (1.2-3.4) in Revised Upper Limb Module (RULM) scores at months 6 and 10. Type III patients exhibited mean changes (95% CI) of 3.9 (2.5-5.3) and 4.3 (2.6-6.0) in HFMSE, and 2.1 (1.2-3.0) and 1.5 (0.0-3.0) in RULM scores at months 6 and 10. Of the 132 patients, 62.9% experienced adverse events (AEs). Two patients experienced mild AEs (aseptic meningitis and myalgia) considered to be related to nusinersen by the investigator, with no sequelae. CONCLUSIONS: These data underscore the significance of nusinersen in Chinese pediatric patients with SMA regarding motor function improvement or stability, and support recommendations on nusinersen treatment by Chinese SMA guidelines and continuous coverage of nusinersen by basic medical insurance.

Gene Expression Profiles Perturbed by Injury to the Mouse Intervertebral Disc.

OBJECTIVES: Back pain subsequent to intervertebral disc (IVD) injury is a common clinical problem. Previous work examining early molecular changes post injury mainly used a candidate marker approach. In this study, gene expression in the injured and intact mouse tail IVDs was determined with a nonbiased whole transcriptome approach. DESIGN: Mouse tail IVD injury was induced by a needle puncture. Whole murine transcriptome was determined by RNASeq. Transcriptomes of injured IVDs were compared with those of intact controls by bioinformatic methods. RESULTS: Among the 18,078 murine genes examined, 592 genes were differentially expressed (P.adj < 0.01). Novel genes upregulated in injured compared with intact IVDs included Chl1, Lum, etc. Ontology study of upregulated genes revealed that leukocyte migration was the most enriched biological process, and network analysis showed that Tnfa had the most protein-protein interactions. Novel downregulated genes in the injured IVDs included 4833412C05Rik, Myoc, etc. The most enriched downregulated pathways were related to cytoskeletal organization. CONCLUSION: Novel genes highly regulated post disc injury were identified with an unbiased approach; they may serve as biomarkers of injury and response to treatments in future experiments. Enriched biological pathways and molecules with high numbers of connections may be targets for treatments post injury.

Multifunctional Wearable Electronic Based on Fabric Modified by PPy/NiCoAl-LDH for Energy Storage, Electromagnetic Interference Shielding, and Photothermal Conversion.

With the rapid advancement of electronic technology, traditional textiles are challenged to keep up with the demands of wearable electronics. It is anticipated that multifunctional textile-based electronics incorporating energy storage, electromagnetic interference (EMI) shielding, and photothermal conversion are expected to alleviate this problem. Herein, a multifunctional cotton fabric with hierarchical array structure (PPy/NiCoAl-LDH/Cotton) is fabricated by the introduction of NiCoAl-layered double hydroxide (NiCoAl-LDH) nanosheet arrays on cotton fibers, followed by polymerization and growth of continuous dense polypyrrole (PPy) conductive layers. The multifunctional cotton fabric shows a high specific areal capacitance of 754.72 mF cm-2 at 5 mA cm-2 and maintains a long cycling life (80.95% retention after 1000 cycles). The symmetrical supercapacitor assembled with this fabric achieves an energy density of 20.83 Wh cm-2 and a power density of 0.23 mWcm-2. Moreover, the excellent electromagnetic interference shielding (38.83 dB), photothermal conversion (70.2 °C at 1000 mW cm-2), flexibility and durability are also possess by the multifunctional cotton fabric. Such a multifunctional cotton fabric has great potential for using in new energy, smart electronics, and thermal management applications.

CD28 costimulation augments CAR signaling in NK cells via the LCK/CD3Z/ZAP70 signaling axis.

Multiple factors in the design of a chimeric antigen receptor (CAR) influence CAR T-cell activity, with costimulatory signals being a key component. Yet, the impact of costimulatory domains on the downstream signaling and subsequent functionality of CAR-engineered natural killer (NK) cells remains largely unexplored. Here, we evaluated the impact of various costimulatory domains on CAR-NK cell activity, using a CD70-targeting CAR. We found that CD28, a costimulatory molecule not inherently present in mature NK cells, significantly enhanced the antitumor efficacy and long-term cytotoxicity of CAR-NK cells both in vitro and in multiple xenograft models of hematologic and solid tumors. Mechanistically, we showed that CD28 linked to CD3Z creates a platform that recruits critical kinases, such as LCK and ZAP70, initiating a signaling cascade that enhances CAR-NK cell function. Our study provides insights into how CD28 costimulation enhances CAR-NK cell function and supports its incorporation in NK-based CARs for cancer immunotherapy.

CoCo-ST: Comparing and Contrasting Spatial Transcriptomics data sets using graph contrastive learning.

Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a target data set representing precancerous tissue. Simultaneously, it mitigates the influence of dominant common patterns shared by the background and target data sets. This enables discerning biologically relevant features crucial for capturing tissue-specific patterns, a capability we showcased through the analysis of serial mouse precancerous lung tissue samples.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

Joint multi-omics discriminant analysis with consistent representation learning using PANDA.

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.

Noncanonical mutations in ribosome nascent peptide exit tunnel confer clarithromycin resistance in Mycobacterium abscessus complex.

OBJECTIVES: Mycobacterium abscessus is a non-tuberculous mycobacterial pathogen that causes pulmonary and skin infections globally. Clarithromycin plays a pivotal role in treating M. abscessus infections, with resistance often leading to treatment failure. While canonical mutations in the 23S rRNA residue 2270/2271 are recognized as the primary mechanism for acquired clarithromycin resistance, resistant isolates lacking these mutations have been widely reported. This study aims to identify new mechanisms of clarithromycin resistance in M. abscessus. METHODS: We selected spontaneous resistant mutants derived from two parental strains characterized by erm(41) T28 and C28 sequevars, respectively. Whole-genome sequencing was performed on mutants lacking the 23S rRNA 2270/2271 mutations. Site-directed mutagenesis was used to confirm the resistance phenotypes of newly identified mutations. Bioinformatic analysis of publicly available genomes was conducted to evaluate the presence of these mutations in clinical isolates. The spatial localization of these mutations in the ribosome was analyzed to investigate potential mechanisms of resistance. RESULTS: A total of 135 resistant mutants were selected from the parental strains. Sequencing of the 78 mutants lacking the 23S rRNA 2270/2271 mutations identified mutations within the peptidyl-transferase center and hairpin loops 35, 49, and 74 of the 23S rRNA. These noncanonical mutations were identified in 57 of 1875 genomes of clinical isolates. Thirteen representative mutations were introduced into the bacterial genome, and their contributions to macrolide resistance were confirmed. The newly identified mutations all localized at the entrance of the nascent peptide exit tunnel, potentially contributing to resistance by disrupting the macrolide binding pocket. CONCLUSION: Several noncanonical 23S rRNA mutations conferring clarithromycin resistance were identified. These mutations enhance our understanding of macrolide resistance in M. abscessus and could serve as important markers for diagnosing clarithromycin resistance.

Metagenomics profiling of the microbial community and functional differences in solid-state fermentation vinegar starter (seed Pei) from different Chinese regions.

Introduction: The starter used in solid-state fermentation (SSF) vinegar, known as seed Pei is a microbial inoculant from the previous batch that is utilized during the acetic acid fermentation stage. The seed Pei, which has a notable impact on vinegar fermentation and flavor, is under-researched with comparative studies on microorganisms. Methods: Herein metagenomics was employed to reveal the microbes and their potential metabolic functions of four seed Pei from three regions in China. Results: The predominant microbial taxa in all four starters were bacteria, followed by viruses, eukaryotes, and archaea, with Lactobacillus sp. or Acetobacter sp. as main functional taxa. The seed Pei used in Shanxi aged vinegar (SAV) and Sichuan bran vinegar (SBV) exhibited a higher similarity in microbial composition and distribution of functional genes, while those used in two Zhenjiang aromatic vinegar (ZAV) differed significantly. Redundancy analysis (RDA) of physicochemical factors and microbial communities indicated that moisture content, pH, and reducing sugar content are significant factors influencing microbial distribution. Moreover, seven metagenome-assembled genomes (MAGs) that could potentially represent novel species were identified. Conclusions: There are distinctions in the microbiome and functional genes among different seed Pei. The vinegar starters were rich in genes related to carbohydrate metabolism. This research provides a new perspective on formulating vinegar fermentation starters and developing commercial fermentation agents for vinegar production.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.

BACKGROUND: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. OBJECTIVE: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. METHODS: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. RESULTS: Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. CONCLUSION: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

A Scalable and Robust Personal Health Management Textile with Multiple Desired Thermal Functions and Electromagnetic Shielding.

The development of functional textiles combining conventional apparel with advanced technologies for personal health management (PHM) has garnered widespread attention. However, the current PHM textiles often achieve multifunctionality by stacking functional modules, leading to poor durability and scalability. Herein, a scalable and robust PHM textile is designed by integrating electrical, radiative, and solar heating, electromagnetic interference (EMI) shielding, and piezoresistive sensing performance onto cotton fabric. This is achieved through an uncomplicated screen-printing process using silver paste. The conductivity of the PHM textile is ≈1.6  ×  104 S m-1, ensuring an electric heating temperature of ≈134 °C with a low voltage of 1.7 V, as well as an EMI shielding effectiveness of ≈56 dB, and human motion monitoring performance. Surprisingly, the radiative/solar heating capability of the PHM textile surpasses that of traditional warm leather. Even after undergoing rigorous physical and chemical treatments, the PHM textile maintains terrific durability. Additionally, the PHM textile possesses maneuverable scalability and comfortable wearability. This innovative work opens up new avenues for the strategic design of PHM textiles and provides an advantageous guarantee of mass production.

Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction.

Language models pretrained by self-supervised learning (SSL) have been widely utilized to study protein sequences, while few models were developed for genomic sequences and were limited to single species. Due to the lack of genomes from different species, these models cannot effectively leverage evolutionary information. In this study, we have developed SpliceBERT, a language model pretrained on primary ribonucleic acids (RNA) sequences from 72 vertebrates by masked language modeling, and applied it to sequence-based modeling of RNA splicing. Pretraining SpliceBERT on diverse species enables effective identification of evolutionarily conserved elements. Meanwhile, the learned hidden states and attention weights can characterize the biological properties of splice sites. As a result, SpliceBERT was shown effective on several downstream tasks: zero-shot prediction of variant effects on splicing, prediction of branchpoints in humans, and cross-species prediction of splice sites. Our study highlighted the importance of pretraining genomic language models on a diverse range of species and suggested that SSL is a promising approach to enhance our understanding of the regulatory logic underlying genomic sequences.

Preliminary exploration of animal models of congenital choledochal cysts.

BACKGROUND: Various animal models have been used to explore the pathogenesis of choledochal cysts (CCs), but with little convincing results. Current surgical techniques can achieve satisfactory outcomes for treatment of CCs. Consequently, recent studies have focused more on clinical issues rather than basic research. Therefore, we need appropriate animal models to further basic research. AIM: To establish an appropriate animal model that may contribute to the investigation of the pathogenesis of CCs. METHODS: Eighty-four specific pathogen-free female Sprague-Dawley rats were randomly allocated to a surgical group, sham surgical group, or control group. A rat model of CC was established by partial ligation of the bile duct. The reliability of the model was confirmed by measurements of serum biochemical indices, morphology of common bile ducts of the rats as well as molecular biology experiments in rat and human tissues. RESULTS: Dilation classified as mild (diameter, ≥ 1 mm to < 3 mm), moderate (≥ 3 mm to < 10 mm), and severe (≥ 10 mm) was observed in 17, 17, and 2 rats in the surgical group, respectively, while no dilation was observed in the control and sham surgical groups. Serum levels of alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, and total bile acids were significantly elevated in the surgical group as compared to the control group 7 d after surgery, while direct bilirubin, total bilirubin, and gamma-glutamyltransferase were further increased 14 d after surgery. Most of the biochemical indices gradually decreased to normal ranges 28 d after surgery. The protein expression trend of signal transducer and activator of transcription 3 in rat model was consistent with the human CC tissues. CONCLUSION: The model of partial ligation of the bile duct of juvenile rats could morphologically simulate the cystic or fusiform CC, which may contribute to investigating the pathogenesis of CC.

Deciphering cell types by integrating scATAC-seq data with genome sequences.

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

Irisin attenuates type 1 diabetic cardiomyopathy by anti-ferroptosis via SIRT1-mediated deacetylation of p53.

BACKGROUND: Diabetic cardiomyopathy (DCM) is a serious complication in patients with type 1 diabetes mellitus (T1DM), which still lacks adequate therapy. Irisin, a cleavage peptide off fibronectin type III domain-containing 5, has been shown to preserve cardiac function in cardiac ischemia-reperfusion injury. Whether or not irisin plays a cardioprotective role in DCM is not known. METHODS AND RESULTS: T1DM was induced by multiple low-dose intraperitoneal injections of streptozotocin (STZ). Our current study showed that irisin expression/level was lower in the heart and serum of mice with STZ-induced TIDM. Irisin supplementation by intraperitoneal injection improved the impaired cardiac function in mice with DCM, which was ascribed to the inhibition of ferroptosis, because the increased ferroptosis, associated with increased cardiac malondialdehyde (MDA), decreased reduced glutathione (GSH) and protein expressions of solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4), was ameliorated by irisin. In the presence of erastin, a ferroptosis inducer, the irisin-mediated protective effects were blocked. Mechanistically, irisin treatment increased Sirtuin 1 (SIRT1) and decreased p53 K382 acetylation, which decreased p53 protein expression by increasing its degradation, consequently upregulated SLC7A11 and GPX4 expressions. Thus, irisin-mediated reduction in p53 decreases ferroptosis and protects cardiomyocytes against injury due to high glucose. CONCLUSION: This study demonstrated that irisin could improve cardiac function by suppressing ferroptosis in T1DM via the SIRT1-p53-SLC7A11/GPX4 pathway. Irisin may be a therapeutic approach in the management of T1DM-induced cardiomyopathy.

Prioritizing genomic variants pathogenicity via DNA, RNA, and protein-level features based on extreme gradient boosting.

Genetic diseases are mostly implicated with genetic variants, including missense, synonymous, non-sense, and copy number variants. These different kinds of variants are indicated to affect phenotypes in various ways from previous studies. It remains essential but challenging to understand the functional consequences of these genetic variants, especially the noncoding ones, due to the lack of corresponding annotations. While many computational methods have been proposed to identify the risk variants. Most of them have only curated DNA-level and protein-level annotations to predict the pathogenicity of the variants, and others have been restricted to missense variants exclusively. In this study, we have curated DNA-, RNA-, and protein-level features to discriminate disease-causing variants in both coding and noncoding regions, where the features of protein sequences and protein structures have been shown essential for analyzing missense variants in coding regions while the features related to RNA-splicing and RBP binding are significant for variants in noncoding regions and synonymous variants in coding regions. Through the integration of these features, we have formulated the Multi-level feature Genomic Variants Predictor (ML-GVP) using the gradient boosting tree. The method has been trained on more than 400,000 variants in the Sherloc-training set from the 6th critical assessment of genome interpretation with superior performance. The method is one of the two best-performing predictors on the blind test in the Sherloc assessment, and is further confirmed by another independent test dataset of de novo variants.

Clinical characteristics of choledochal cysts with intrahepatic bile duct dilatations: an observational study.

Purpose: Whether a dilated intrahepatic bile duct (IHBD) has any effect on the prognosis of choledochal cyst (CC) remains controversial. We aimed to summarize the clinical characteristics and prognosis of CC with IHBD dilatation. Methods: One hundred ninety-two children diagnosed with CC were identified, including 127 without IHBD dilatation (group A) and 65 with IHBD dilatation (group B). A retrospective analysis was performed to explore the clinical characteristics and prognosis of CC with IHBD dilatation based on clinical indices, symptoms, and complications. Results: Compared with group A, incidences of jaundice and fever were higher in group B (P = 0.010 and P = 0.033). Preoperative total bilirubin, direct bilirubin, and indirect bilirubin were increased in group B compared to group A (P = 0.005, P < 0.001, and P = 0.014), as were preoperative ALT, AST, γ-GT, and total bile acid (P = 0.006, P = 0.025, P < 0.001, and P = 0.024). The risk of liver fibrosis or cirrhosis was significantly increased for group B compared with group A (P = 0.012) and also occurred earlier in group B (P = 0.006). In the dilated IHBDs, 95.4% (62 of 65) recovered to normal, and more than half of dilated IHBDs (37 of 65) recovered to normal in 1 week. Conclusion: Most IHBDs can recover to normal postoperatively in a short time, and proactive treatment is recommended for CC patients with IHBD dilatation for significant abnormal liver functions.